narelle kay eggins b.soc.wk.; dip648882/s... · and lifelong career development. the populist...

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Lifelong Career Mobility and Change Options in Knowledge Occupations:

Career Pathway Implications of Initial Degree Choices.

Narelle Kay Eggins

B.Soc.Wk.; Dip.Psych

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2016

School of Education

ii

Abstract

Because of the social, personal and financial benefits of paid work activities, occupational choices,

and lifelong career options warrant careful consideration. Rapid social, technological and

organisational change is theorised to produce changes in the world of work where future, or ‘new

careers’ will be characterised by high levels of mobility; and the demand for knowledge workers

(with a university qualification) in a ‘knowledge economy’ with be high. Paradoxically there is

some agreement that the level of mobility varies between levels of occupations, and is inversely

related to education level, so that mobility would be lowest in knowledge occupations. However,

from inadequate definitional precision, the type of workforce mobility referred to in these

predictions and observations is unclear. In the ‘new careers’, individuals are increasingly seen to

have the freedom and opportunity to construct and manage their own careers lifelong by adding

skills and knowledge to an existing set, changing sets, or changing mode of practice, for example to

self-employment, or relocating. However knowledge occupations tend to be bounded by mandatory

qualifications and other controls, which could make the option of mobility by changing skills and

knowledge sets at level, costly in time and money. Factors such as age and gender have also been

found to affect choices of field of study and employability on graduation.

Information about the level and nature of future mobility options is important for individuals

contemplating high initial investments of time and money in their education for labour market entry

and lifelong career development. The populist current and future mobility discourse propagated

politically and in the media, has the potential to misinform university aspirants about the type and

level of their lifelong career pathway options, and warrants clarification.

The present study examined the current situation regarding skills and knowledge- set adjustments

and changes in knowledge occupations by Australian university graduates, by focusing on skills-

and-knowledge-based mobility and change among knowledge occupations. Fields of qualifications,

rather than self-reported names of occupations were used as more accurate indicators of actual skills

and knowledge sets. The fields of past and recent qualifications of individuals who had undertaken

further credentialing were compared to determine the extent to which people with qualifications in a

particular field actually chose further study in a different field, as an indication of future

occupational intent and of perceived change options. This research also explored the motivations for

studying a different skills and knowledge- set, and the influence of demographic, contextual and

social factors. A mixed method design was employed and undertaken in two phases. Phase 1, a

quantitative study, sourced the Australian Graduate Survey responses as a large secondary data base

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from which patterns of change between educational fields were examined together with their

association with demographic and contextual factors such as age, gender, and cost. In phase 2, a

qualitative study, semi-structured interviews were conducted with a purposive sample of nine

individuals who had undertaken extra credentialing in a field that was different to that of their past

study, to examine the reasons for their decision, and issues surrounding the undertaking.

Significantly more than half of the questionnaire respondents had not changed field. Of the others,

patterns of change varied between fields of study, age and gender with many suggestive of add-

skilling for career advancement in the initial occupation rather than reskilling for occupational

change. Most change was from generic to specific type degrees. Interview participants had

changed field because of dissatisfaction with the outcomes from their initial qualification, and chose

their field of second study to remedy those problems. The financial aspects of undertaking the

change-in the context of life stage tasks were prime considerations in the change decision. Thus

actual reskilling among knowledge occupations was costly, disruptive and relatively rare.

If as this research indicates, skills-and-knowledge-based mobility options in high knowledge

occupations are limited by their unique features, the predictions of a future of ‘new (very mobile)

careers’, developing concurrently with a ‘knowledge economy’ warrant further careful

consideration and empirical exploration to determine their combined practical and universal

applications in the world or work. This research has contributed a more evidenced-based

understanding of skills-and-knowledge-based mobility in knowledge occupations, and of factors

influencing related lifelong occupational options to the field of career development; and has raised

questions about the future predictions for mobility in a changing world of work. Implications for

theory, practice and policy are discussed together with the contributions of the study and

suggestions for further research. Limitations are acknowledged.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published or

written by another person except where due reference has been made in the text. I have clearly

stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical

assistance, survey design, data analysis, significant technical procedures, professional editorial

advice, and any other original research work used or reported in my thesis. The content of my thesis

is the result of work I have carried out since the commencement of my research higher degree

candidature and does not include a substantial part of work that has been submitted to qualify for

the award of any other degree or diploma in any university or other tertiary institution. I have

clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and,

subject to the policy and procedures of The University of Queensland, the thesis be made available

for research and study in accordance with the Copyright Act 1968 unless a period of embargo has

been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright

holder(s) of that material. Where appropriate I have obtained copyright permission from the

copyright holder to reproduce material in this thesis.

v

Publications during candidature

No Publications

Publications included in this thesis

No publications included

Contributions by others to the thesis

No contributions by others.

Statement of parts of the thesis submitted to qualify for the award of another degree

None

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Acknowledgements

I would like to express my gratitude to my supervisors: Dr. Mary McMahon, my principal

supervisor; and Professor Michele Haynes. Mary agreed to work with me despite my many years

away from university, and thus relative academic naivety, which was no doubt amusing and

frustrating at times. Nevertheless she was always very pleasant to work with, patient, flexible and

encouraging throughout. Her extensive knowledge and experience in the field of career was

invaluable, especially during the design phase, and her ongoing support, and understanding in some

difficult family times was greatly appreciated. I also greatly valued Professor Michele Haynes’s

agreeing to take on a supervisor role despite the many demands on her time. Her reassurance were

very important and her advice on cleaning up a large data base, on a suitable statistics package and

on statistical procedures were vital. I found her ‘down to earth’ comments and attitude to the thesis

topic generally refreshing. Thanks also goes to my panel, Dr. Kim Nichols & Associate Professor

Monica Cuskelly for their interest, feedback and encouragement, and for making each milestone a

rewarding experience.

I am indebted to my participants who are all busy people, for their time and their willingness to

share their stories, without which there wouldn’t be a thesis.

I want also to express my gratitude for the support, caring and advice of my friends Judy and Diana,

who were always there for me when I needed them.

This thesis is dedicated to my family: to my dear parents who sadly both died before submission,

my father very recently; to our son, Simon, and daughter, Stephanie, who always believed I’d get

there; and most particularly to my husband, Ron. Without his love and support in so very many

ways, from computer programming and troubleshooting ++, chauffeuring, and attending to my

parents’ needs, to tolerating my not infrequent expressions of exasperation, I doubt I would have

persisted.

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Keywords

Knowledge occupations, education, opportunity cost, lifelong career, occupational mobility/change,

skills-and-knowledge-sets, boundaryless & protean models, life-stage, fields of occupation

Australian and New Zealand Standard Research Classifications ANZSRC)

ANZSRC code 130103 Higher Education, 90%

ANZSRC code: 130305 Educational Counselling, 10%

Fields of Research (FoR) Classification

1303 Specialist Studies in Education, 100%

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Table of Contents

Abstract .................................................................................................................................... ii

Chapter 1. Introduction to the Research Problem............................................................ 14

1.1 Globalisation and Changes in the World of Work ....................................................... 15

1.1.2 An era of mobility? ............................................................................................... 16

1.1.3 Is the predicted mobility skilled-based? ................................................................ 17

1.2.1 Lack of definitional precision. .............................................................................. 18

1.2.2 Terminology. ......................................................................................................... 19

1.3 Assessing Occupational Mobility ................................................................................ 21

1.3.1 Implications of mobility options for university aspirants. .................................... 22

1.4 Rationale of the Research .......................................................................................... 23

Chapter 2. Occupation Mobility and Change in Knowledge Occupations ..................... 25

2.1 Features of Knowledge Occupations ......................................................................... 25

2.2 Mobility: A Generic Term with Many Aspects ........................................................... 27

2.2.1 The nature of occupational mobility from a historical perspective. ..................... 28

2.2.2 Organisational change. .......................................................................................... 29

2.2.3 The traditional model. ........................................................................................... 29

2.2.4 Self-managed, mobility focused career models. ................................................... 29

2.2.5 Vocational Psychology: Mobility by Adaption. ................................................... 34

2.2.6 The Mobility Discourse. ....................................................................................... 35

2.3 The Economic Perspective ........................................................................................... 37

2.3.1 Human capital theory. ........................................................................................... 38

2.3.2 Transferability of skills and the cost of change. ................................................... 39

2.4 How important is the initial choice? ............................................................................ 40

2.5 Generalist Verses Specific-skill Degrees ..................................................................... 42

2.5.1 The career trajectory benefits of generalist vs specialist degrees. ........................ 42

2.5.2 Skills transferability and the generic/specific degree debate. ............................... 43

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2.5.3 Questioning the functionality of a liberal arts qualification. ................................ 43

2.6 Lifelong Learning: A Mechanism of Occupational change? ....................................... 44

2.7 Person-in-Situation Factors Influencing Occupational Mobility ................................. 45

2.7.1 Age. ....................................................................................................................... 45

2.7.2 Gender. .................................................................................................................. 47

2.7.3 Other contextual factors. ....................................................................................... 48

2.8. Motivations for Changing Occupation ........................................................................ 49

2.8.1 Adding a business qualification. ........................................................................... 49

2.8.2 Teaching as a change option. ................................................................................ 50

2.9 Summary of Literature Review .................................................................................... 51

Chapter 3. Method ............................................................................................................... 53

3.2 Study Design ................................................................................................................ 53

3.2.1 Phase One: The quantitative study. ....................................................................... 54

3.2.2 Phase two: The qualitative study. ......................................................................... 65

3.3 Chapter Summary ........................................................................................................ 73

Chapter 4. Results of Phase 1: Broad fields and Patterns of Change ............................. 74

4.1 Patterns of Change in Broadfields of Study ................................................................. 74

4.1.1 The proportions of individuals changing broadfield. ............................................ 74

4.2 Categorising Broadfields.............................................................................................. 85

4.2.1 Group 1: Broadfields with high negative change ratios. ...................................... 86

4.2.2 Group 2: Broadfields with high positive change ratios......................................... 88

4.2.3 Group 3: Broadfields with relatively low change ratios. ...................................... 91

4.2.4 Summary of findings on patterns of broadfield change. ....................................... 93

4.3 Patterns of Change Between Broadfields and Selected Narrow-fields. ....................... 93

4.3.1 The narrow-fields within society and culture. ...................................................... 93

4.3.2 Narrow-fields within other broadfields. ................................................................ 98

4.4 Chapter Summary ...................................................................................................... 100

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Chapter 5. Change of Broad Field in Context ................................................................. 101

5.1 The Relationship between Participant Age and change or No change of broadfield 101

5.1.1 The age distribution of the sample. ..................................................................... 101

5.1.2 Age groups and broadfields. ............................................................................... 103

5.1.3 Age groups and change of broadfield. ................................................................ 106

5.1.4 Age and changes into all broadfields. ................................................................. 107

5.1.5 The relationship between age groups and groups of broadfields. ....................... 108

5.1.6 Age and pre-planned programmes. ..................................................................... 123

5.1.7 Summary of the influence of age. ...................................................................... 124

5.2 Gender ........................................................................................................................ 124

5.2.1. Gender, age groups and changes of broadfields. ............................................... 124

5.3 Fee Payment Options ................................................................................................. 129

5.3.1 Summary Statistics .............................................................................................. 130

5.3.2 Course fees and payment methods in association with age groups. ................... 131

5.3.3 Methods of paying for the course and broadfields. ............................................. 131

5.4 Full-time/Part-time Study .......................................................................................... 131

5.4.1 Full-Time/part-time study and change of broadfield. ......................................... 132

5.5.2 Age and full/part time study................................................................................ 133

5.5.3 Gender and full/part-time study. ......................................................................... 135

5.5 Chapter Summary ...................................................................................................... 135

Chapter 6. Results of Phase 2: The Qualitative Study.................................................... 136

6.1 An Introduction to the Story of each Participant. ...................................................... 136

6.2 Thematic Structure ..................................................................................................... 140

6.3 Main Theme: Decision-Making Processes ................................................................ 142

6.3.1 Theme: Consultation. .......................................................................................... 142

6.3.2 Theme: Individual approaches. ......................................................................... 143

6.3.2 Summary of the main theme. .............................................................................. 148

6.4 Main Theme: Returning to Study............................................................................... 148

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6.4.1 Theme: Reasons for changing broadfield. .......................................................... 148

6.4.2 Theme: Choosing a change qualification. ........................................................... 153

6.5 Chapter Summary ..................................................................................................... 158

Chapter 7. Change Processes. ........................................................................................... 160

7.1 Main Theme: Individual-in-Situation ....................................................................... 160

7.1.1 Theme: Age. ........................................................................................................ 160

7.1.2 Theme: Financial issues. ..................................................................................... 167

7.1.3 Theme: Gender. ................................................................................................... 172

7.2 Main theme: Outcomes of the Change ....................................................................... 172

7.2.1 Theme: Employment following change qualification. . ..................................... 172

7.2.2 Theme: Perceived skills-and-knowledge-based nature of change. ..................... 174

7.2.3 Theme: Satisfaction with outcome. ..................................................................... 176

7.3 Chapter Summary: Phase 2 Data Analysis ................................................................ 177

Chapter 8. Discussion ........................................................................................................ 179

8.1 Broadfield Changes and Patterns of Change .............................................................. 179

8.1.1 Percentage changing broadfield .......................................................................... 179

8.1.2 Patterns of broadfield change. . ........................................................................... 180

8.1.3 Group 1 broadfields: science, engineering (STEM), creative industries. ........... 181

8.1.4 Group 2 broadfields: business; education; health; architecture. ......................... 183

8.1.5 Group 3 broadfields: society and culture ............................................................ 184

8.2 Identifying possible types of change .......................................................................... 185

8.3 The Generic Versus Specific Skill Dichotomy .......................................................... 186

8.3.1 The change direction: generic to specific-skill courses. ..................................... 187

8.4 Why Participants Returned to Study in a Different Broadfield ................................. 187

8.4.1 Ineffective decision-making processes. .............................................................. 187

8.4.2 Dissatisfaction with initial degree. ...................................................................... 189

8.4.3 Dissatisfaction with occupational outcomes ....................................................... 190

8.4.4 What participants hoped to achieve by changing. ............................................... 191

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8.5 Factors Relating to the Decision to Change and the Change Process ........................ 192

8.5.1 The Interrelationship between age and changing broadfield. ............................. 192

8.5.2 Summary of age and age-related issues. ............................................................. 197

8.6 The Interrelationship between Gender and Changing Broadfield ............................. 197

8.6.1 Gender differences in broad-field change choices. ............................................. 198

8.6.2 Social norm expectations and gender-based differences in outcome. ................ 199

8.7 The Interrelationships Between Financial Issues and Changing Broadfield ............. 200

8.7.1 Financial characteristics of particular professions that affect their appeal as

change qualifications.................................................................................................... 201

8.8 Participants’ change outcomes ................................................................................... 202

8.8.1 Were the Outcomes Disjunctive? ........................................................................ 202

8.9 Low Skills-and-knowledge-based Mobility: Implications for Mobility Models ....... 203

8.10 Summary of the Discussion ..................................................................................... 204

Chapter 9. Project Summary, Conclusions, and Implications ....................................... 206

9.1 Outcomes of the Research .......................................................................................... 207

9.2 Contributions of the Research .................................................................................... 208

9.2.2 Contributions to career theory............................................................................. 209

9.2.3 Contributions to lifelong learning theory. ........................................................... 212

9.2.4 Contributions to practice ..................................................................................... 212

9.3 Implications for Policy ............................................................................................... 214

9.5 Suggestions for Further Research .............................................................................. 218

9.6 Conclusions ................................................................................................................ 219

References ........................................................................................................................... 220

Appendices .......................................................................................................................... 279

Appendix 1. Semi-structured Interview Schedule .......................................................... 279

Appendix 2. ...................................................................................................................... 282

Qualifications accepted as Past study at Least at Bachelor Degree Level ....................... 282

Appendix 3: Forms........................................................................................................... 284

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Appendix 4. Ethical Clearance ......................................................................................... 288

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List of Tables

Table 3.1 Australian University Groupings ....................................................................................... 58

Table 3.2 Responses that led to exclusion.......................................................................................... 60

Table 3.3 Table of Analyses ............................................................................................................... 63

Table 3.4 Development of the Thematic Structure ............................................................................. 71

Table 4.1 Overall Past and Recent Study Matrix showing the numbers of individuals with past and

recent study in all Broadfields. The proportions in brackets on the diagonal refer to the

proportion of individuals who did not change broadfield. ........................................................ 76

Table 4.2 Proportion changing out of broadfields compared with overall proportion changing

(0.48) .......................................................................................................................................... 82

Table 4.3 Proportion changing into broadfields compared with overall proportion changing (0.48)

.................................................................................................................................................... 82

Table 4.4 Past Study Cohorts and % Changing Out.......................................................................... 84

Table 4.5 Recent Study Cohorts and % Changing In ........................................................................ 84

Table 4.6 Groups of Broadfields ........................................................................................................ 86

Table 4.7 Changes out of Science ...................................................................................................... 87

Table 4.8 Changes out of Engineering ............................................................................................... 88

Table 4.9 Changing Field into Business ............................................................................................ 89

Table 4.10 Changing Field into Education ........................................................................................ 90

Table 4.11 Changing Field into Health ............................................................................................. 91

Table 4.12 Changing Broadfield into society and culture ................................................................. 93

Table 4.13 Past and Recent Study Matrix showing the numbers of individuals with Past and Recent

Study in the narrow-fields of society and culture ...................................................................... 95

Table 4.14 Larger narrow-fields (NF) of Society and Culture and Change/No Change .................. 96

Table 4.15 Broadfields Past Study Cohorts and Recent Study in narrow-fields of Society & Culture

.................................................................................................................................................... 97

Table 4.16 Narrow-fields of Past Study in Society & Culture and Broadfield Recent Study Cohorts

.................................................................................................................................................... 98

Table 5.1 Distribution of Age Groups across Broad Felds in the Past Study Cohort (%) .............. 104

Table 5.2 Distribution of Age Groups across Broad Felds in the Recent Study Cohort (%) .......... 104

Table 5.3 Percentage Age Group Composition of Past Study Cohort of Each Broadfield (%) ...... 105

Table 5.4 Percentage Age Group Composition of Recent Study Cohort of Each Broadfield (%)... 105

Table 5.5 Age groups and change: Odds ratios............................................................................... 107

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Table 5.6 Age Group make-up of Recent Study Cohorts with Change of Broadfield(%) (Chamge-in)

.................................................................................................................................................. 108

Table 5.7 Groups of broadfields ...................................................................................................... 109

Table 5.8 The Numbers and Percentages of 21-23 year olds changing into education .................. 124

Table 5.9 The Numbers and Percentages of 25-27 year olds changing into Medicine ................... 124

Table 5.10 Percentages of Males and Females Changing into all Broadfields .............................. 127

Table 5.11 Frequency of Types of Fees ........................................................................................... 130

Table 5.12 The Relationship Between Age and Full Time/Part Time study .................................... 134

Table 6.1 Participants’ Broadfield Changes with Narrow-fields Indicated .................................... 139

Table 6.2 Age at Commencement of Initial and change Degrees and at interview ......................... 140

Table 6.3 Course length and Mode of Study for Change Qualification .......................................... 157

Table 7.1 Methods of Payment of Course Fees and Remaining Debt for all Participants .............. 168

Table 8.1 Groups of broadfields ...................................................................................................... 181

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List of Figures

Figure 4.1. Changes out of and into the broadfield of Science (as an example of the change process)

.................................................................................................................................................... 78

Figure 4.2. The Proportional Broadfield Composition of the Change-out Cohort ............................ 79

Figure 4.3. The Proportional Broadfield Composition of the Change-in Cohort. ............................. 79

Figure 4.4. Change ratios by broadfield ............................................................................................ 85

Figure 5.1. Age Distribution of the Sample ..................................................................................... 102

Figure 5.2. Frequency of respondents by age group........................................................................ 103

Figure 5.3: The Percentages Changing and not Changing Broadfields by Age Group ................... 107

Figure 5.4. Percentages of Changers in Each Age Group Changing OUT of all Broadfields. ....... 110

Figure 5.5: Percentages of Changers in Each Age Group Changing INTO all Broadfields .......... 111

Figure 5.6. Percentage Age Group Composition of Changes OUT of Science, Engineering, and

Creative Industries ................................................................................................................... 113

Figure 5.7. Numerical Changes into and out of Science ................................................................. 114

Figure 5.8. Percentages of Change from Science to Health by Age Group .................................... 115

Figure 5.9. Percentage Age Group Composition of Changes INTO Education, Business, and Health

.................................................................................................................................................. 116

Figure 5.10. Percentage Age Group Composition of Changes INTO Business .............................. 118

Figure 5.11. Percentage Age Group Composition of Changes from Society and Culture INTO

Education ................................................................................................................................. 119

Figure 5.12. Percentage Age Group Composition of Changes from Creative Industries INTO

Education ................................................................................................................................. 119

Figure 5.13. Percentage Age Group Composition of Changes from Business INTO Education .... 119

Figure 5.14. Percentage Age group Composition of Changes IN and OUT of Society and Culture

.................................................................................................................................................. 120

Figure 5.15. Proportional age group Composition of changes INTO narrow-fields ....................... 122

Figure 5.16. Proportional Age Group Composition of Changes IN to and OUT of Information

Technology............................................................................................................................... 123

Figure 5.17. Age, Gender and No change (NO)/change (CH) ........................................................ 125

Figure 5.18: Age Group Percentages and Gender with Change of Broadfield ............................... 126

Figure 5.19. Changes INTO broadfields under 30 years ................................................................. 128

Figure 5.20. Changes INTO broadfields Age 30-39 years .............................................................. 128


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Figure 5.23. Changes INTO broadfields Ages over 59 years .......................................................... 129

Figure 5.24. Percentage age group Composition of Fee Pay-Type Categories with change .......... 131

Figure 5.25. Percentages of Full-Time/Part-Time Study where Broadfield was Not Changed ...... 132

Figure 5.26. Percentages of Full-Time/Part-Time Study Where There was a Change of Broadfield

.................................................................................................................................................. 132

Figure 5.27. Percentages of Full-Time/Part-Time Study by Age-group with No change of

Broadfield................................................................................................................................. 133

Figure 5.28. Percentages of Full-Time/Part-Time Study by Age-group with Change of Broadfield.

.................................................................................................................................................. 133

Figure 5.29. Gender and Full /Part time study with change of broadfield. .................................... 135

Figure 6.1. Thematic Structure ........................................................................................................ 141

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List of Abbreviations Used in the Thesis

Abbreviation

Full Name

ABS Australian Bureau of Statistics

AGS Australian Graduate Survey

AQF Australian Qualifications Framework

ASCED Australian Standard Classification of Education

B.A. Bachelor of Arts

B.Sc Bachelor of Science

B.Soc.Sc Bachelor of Social Studies

BIT Bachelor of Information Technology

CPD Continuing Professional Development

CSM Career Self-Management

Dip.Ed (Graduate) Diploma of Education

GCA Graduate Careers Australia

GP General Practitioner (medical)

GPA Grade Point Average

HECS Higher Education Contribution Scheme

HILDA The Household, Income and Labour Dynamics in Australia

HR Human Resource

LA Los Angeles

Maths Mathematics

MBA Master of Business Administration

https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&ved=0ahUKEwjFp82ahbnPAhWMkZQKHYhpA7kQFggtMAU&url=https%3A%2F%2Fwww.dss.gov.au%2Four-responsibilities%2Ffamilies-and-children%2Fprogrammes-services%2Fthe-household-income-and-labour-dynamics-in-australia-hilda-survey&usg=AFQjCNE0NU_vxCgqfTNBLlYAgvEia_6B0Q&bvm=bv.134495766,d.dGo

12

MBBS Bachelor of Medicine/Bachelor of Surgery

MI Motivational Interviewing

MMD Mixed Method Design

MPA Master of Professional Accounting

NF Narrow-field

OECD Organisation for Economic Co-operation and Development

OP Overall Position (tertiary entry)

PE Physical Education

QTAC Queensland Tertiary Admission Centre

RMIT Royal Melbourne Institute of Technology

STEM Science Technology Engineering and Mathematics

TER Tertiary Entrance Rank

UCLA University of California Los Angeles

UK United Kingdom

uni university

USA United States of America

VET Veterinarian

https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&sqi=2&ved=0ahUKEwjg69vxhLnPAhXKk5QKHUfKCTQQFghCMAk&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FOrganisation_for_Economic_Co-operation_and_Development&usg=AFQjCNESxRKiP-XR-UAyq6SZUny1rXLefg&bvm=bv.134495766,d.dGo

13

Abbreviations of

Broadfields

Broadfields of Qualifications

Sc Science

IT Information Technology

Eng Engineering

Arch Architecture and Building

Agri Agriculture and Environmental Studies

Heal Health

Edu Education

Bus Business

S&C Society and Culture

CI Creative Industries

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Chapter 1

Introduction to the Research Problem

Work can provide a sense of identity, social status, self-worth, purpose, challenge, self-

fulfillment, access to social networking and of course, income (Baruch, 2004). Combined, these

correlates of paid work highlight the importance of occupational decisions made by individuals

through their lives in the process of career development (Bimrose & Mulvey, 2015; Gottfredson,

1996; van Vianen, De Pater, & Preenen, 2009). Theoretical modelling of the processes of career

development has been a focus of the discipline of career studies, but the field tends to offer mainly

descriptive theories and models (Baruch, Szűcs, & Gunz, 2015), lacking validation from empirical

studies particularly on elements of the models.

The notion of people developing their careers has been variously described (Patton &

McMahon, 2014), and implies the ability, facility and opportunity (Moscarini & Thomsson, 2007;

Rosenfeld, 1992; Sullivan & Arthur, 2006) to undertake ongoing adjustments in accordance with

the personal and contextual aspects of their lives through their lifespan. The relative weight of

individual preferences and the influences of economic, structural, cultural, and social influences on

career outcomes is still debated (Gerber, Wittekind, Grote, Conway, & Guest, 2009; Inkson, Gunz,

Ganesh, & Roper, 2012; Mayrhofer, Meyer, & Steyrer, 2007), though increasingly the focus

appears to be shifting away a little from the idea that individuals have the total freedom and control

to the manage their careers lifelong (Tams & Arthur, 2010, p. 630).

As the concept of career development implies change, it assumes the availability of the

element of job or occupational mobility in the work context. In the last two decades, there has been

increased attention to levels of occupational mobility in the career literature, with theoretical models

proposed in response to perceived changes in organisational structures (Clarke, 2013; Rodrigues &

Guest, 2010), globalisation, and what is commonly referred to as the digital revolution (Craw,

2016). From this rapid change narrative, the future world of work has been forecast to be made up

of ‘new careers’ characterised by ongoing mobility and change (Arthur & Rousseau, 1996a; Gubler,

Arnold, & Coombs, 2014b) concurrent with development of a ‘knowledge economy’ (Gibb &

Walker, 2011) with ever increasing demand in ‘knowledge occupations’ for ‘knowledge workers’

(Arthur & Rousseau, 1996b; Baldwin & Beckstead, 2003). With no clear consensus on the

definition of knowledge workers, they have been variously described in terms of occupational

analyses based on complexity of job tasks (Beckstead & Vinodrai, 2003) or, as employed in this

study, in terms of education level, as workers with university qualifications (Paul, 2011; Salt, 2015).

There are indications that the move to higher workforce concentrations of knowledge workers has

15

been evolving in the last three decades in developed economies (Baldwin & Beckstead, 2003) with

current trends in the UK (Purcell, Wilton, & Elias, 2007) and Australia towards an era of mass

tertiary education, with over 50% of Australian school leavers choosing university study (Finkel,

2016). Simultaneously, however, there is growing evidence of over qualification, underutilisation of

graduates’ skills, increasing employment of graduates in non-graduate occupations, and lowered

financial returns for graduates on their investment in university qualifications (Green & Zhu, 2010;

O’Leary & Sloane, 2016). The occupational structures of labour markets and the demands for

particular higher skills and knowledge change over time, for example, in the UK (Elias & Purcell,

2013), so that with increasing competition for degree-related employment (Finkel, 2016), mobility

options particularly the ease of changing to other fields, becomes a major issue for graduates.

While there are differing views on the comparative mobility levels for different occupational

levels and at different career stages (Hachen, 1990), there is some agreement in the literature that

the level of mobility varies between levels of occupations, and is inversely related to education level

(Kambourov & Manovskii, 2008; Sicherman & Galor, 1990; Super, 1980b; Sweet, 2011). Therefore

levels of mobility tend to be lowest in these high knowledge occupations. Hence the two future

predictions would seem to constitute a theoretical quandary: highly mobile ‘careers’ in a world of

work when the fastest occupational growth is in ‘knowledge occupations’ which are thought to have

the lowest level of mobility. To improve understanding of one side of this dilemma, this study

focusses on enhanced understanding of the realities of mobility options for knowledge workers,

particularly in relation to occupational change.

A better understanding of the type and availability of mobility options in knowledge

occupations would also enable individual university aspirants to make more informed initial

programme choices and later career plans to maximise benefits from their investment (Fenesi &

Sana, 2015) and minimise further costs. Chapter 1 introduces the topic of occupational mobility as

it features in theoretical models of career, against a background of current predictions of future

changes in the world of work resulting from globalisation and rapid technological change. An

examination of mobility is then presented in the context of the multidisciplinary nature of career

and the resultant definitional issues, followed by the rationale for the research.

1.1 Globalisation and Changes in the World of Work

Related to globalisation, the rapidity of economic, technological and social change is

theorised to have resulted in changes in the world of work in advanced economies (Chesters, 2014).

The next section considers these described and predicted changes and the nature of the necessarily

increased occupational mobility implied.

16

1.1.2 An era of mobility? Deindustrialisation under way in many Western economies

(Bachmann & Burda, 2007; Bachmann & Burda, 2010; Greenaway, Upward, & Wright, 2000) has

been paralleled by growth in the service sector (Lee & Wolpin, 2006) with a large reduction in the

growth of low skilled jobs and a much increased demand for highly skilled professionals and

managers (Chesters, 2014). However, data shows growth in the number of low skilled jobs in the

service sector in Australia (JobOutlook, 2012). Relatedly, the projected fastest growth in jobs in the

USA, 2014-2024, is in the health sector at all skills and knowledge levels, with the information

technology sector predicted to be the third fastest growth industry, equal to personal care services

(Bureau of Labor Statistics, 2015). New occupations have evolved to coincide with rapid

developments in information technology and automation (Adams & Demaiter, 2008; Brown, 2002;

Parrado, Caner, & Wolff, 2007). There has also been growth in global careers, particularly

managers, as part of the globalisation of business (Suutari & Mäkelä, 2007), locations of jobs have

changed, and there have been changes in employment structures with moves away from tenured

positions to more contract work and self-employment (Mallon, 1999). This is “an era when

mobility and self-driven careers are a major focus of attention” (Gubler, Arnold, & Coombs, 2014a,

p. 642).. The present time is also referred to as the “Digital Age” (World Economic Forum, 2016),

perhaps implying that many of the forecast changes result from rapid improvements in digital

technology.

Several models (e.g., boundaryless careers, protean careers, and life designing) have been

theorised to describe the impact of the predicted increasing mobility in the world of work on career

structures and processes. The notion of increasing mobility has permeated the career literature for

nearly two decades, though the focus on mobility has been more prominent in the career discussions

from the management perspective (Inkson et al., 2012).

This increased interest in mobility has been attributed by some (Arthur, 2014; Inkson et al.,

2012; Inkson, Roper, & Shiv, 2009; Kattenbach, Lücke, Schlese, & Schramm, 2011; Okay-

Somerville & Scholarios, 2014; Rodrigues, Guest, & Budjanovcanin, 2015; Smith-Ruig, 2009) to

the proposal of the boundaryless model of career development by Arthur (1994) and Arthur and

Rousseau (1996a) in response to changes they predicted in the structure of organisations (e.g.,

delayering and downsizing). However, in keeping with criteria for discourse analysis (Parker,

1990), the boundaryless model has also been described as a ‘discourse’ (Inkson et al., 2009;

Richardson, 2012).

In support, Mayrhofer castigated career, human resource management, and organisational

behaviour researchers for “Falling for the change Hype” (Mayrhofer, 2012, p. 77). He argued that

empirical evidence negated the narrative of rapidly increasing changes in careers, and suggested

17

that such a narrative was particularly attractive to researchers as it created new issues to examine,

and legitimised requests for funding. Despite the increased mobility discourse, there were no

increases in job turnover in the USA, Japan or Europe at least among managers and professionals

between 1992 and 2006 (Rodrigues & Guest, 2010).

Also, even with suggestions of differences in mobility levels with different occupational

groups or levels, there has been little attention to the skills-based nature of the predicted increases in

mobility, and thus to any potential complexities relating to particular occupational groups. Further,

the discourse does not appear to differentiate between the skills-based nature of occupational/job

changes, and changes in the method of work delivery, for example, contracting or portfolio work,

which could give a false impression of the nature, and level of mobility. For example, Thompson

(1968) used the term ‘mobility’ almost exclusively to refer to physical relocation while almost 40

years later, Sullivan and Arthur (2006) included moves between relocations, in their predictions of

future mobility.

The mobility discourse has been variously applied to most aspects of work and careers (e.g.,

Kelan & Jones, 2009; Muja & Appelbaum, 2014a) despite the absence of clear definitions, and

without questioning or testing of the practical applications of the mobility models and the related

concept of the ‘new careers’ (Feldman & Ng, 2007). Comparative estimations of mobility levels

between groups and countries have also suffered from non-standard terminology (Lalé, 2012), and

more recent studies have suggested the claims about the changed nature of new or modern careers

are rather exaggerated (Lyons, Schweitzer, & Ng, 2015). Clearly, the levels and nature of

occupational mobility need to be better understood.

1.1.3 Is the predicted mobility skilled-based? What is not clear, then, is the skills-based

relationship between successive jobs/occupations, and importantly, whether the increased levels of

mobility apply equally to movement between all levels and fields of occupation. Of interest in this

study is mobility in knowledge occupations, which has alternatively been found to be generally:

low (Elliott & Lindley, 2006; Feldman & Ng, 2007; Neal, 1999; Super, 1980b)

low, but increasing (Parrado et al., 2007)

high in Britain but varying between countries depending on the level of occupational

specificity of education (Leuze, 2007)

varying according to the field of occupation (Bachmann & Burda, 2007; Elliott &

Lindley, 2006)

varying according to the source of education (corporate or individual),

demographics, and to various job related contextual issues (Dolton & Kidd, 1998).

18

Knowledge occupations are unique in the high level of initial investments of money and

time required to achieve the prerequisite qualifications. The high costs of tertiary education mean

that considering the realistic, lifelong career development implications of initially investing in a

university qualification for entry to the labour market is important for university aspirants. In view

of the current mobility discourse, the potential for increasing but unspecified workforce mobility in

knowledge occupations could in turn influence career decision-making of university aspirants. This

potential for increasing mobility tends to be assumed in theoretical models of career.

It is likely that choices made about ‘adding skills and knowledge’ or ‘changing skills and

knowledge sets’ may differ between different sectors of work (Kattenbach et al., 2014), and may

depend on the way that employment in knowledge occupations is organised (Minten & Forsyth,

2014). One of the difficulties in understanding the nature of occupational mobility within

individual careers, is the nature of this literature which is informed by a range of disciplines

including psychology, sociology, economics, management and business, all of which approach the

topic from their own particular orientations (Baruch et al., 2015; Schein, 2007).

1.2 The Multidisciplinary Nature of the Career Literature

There are several different perspectives from which ‘careers’, occupational change, choice

and mobility have been approached (Ornstein & Isabella, 1993). The discipline of vocational

psychology studies occupational choices from the perspective of the individual, which distinguishes

it from other disciplines such as organisational psychology which focuses on occupational mobility

from the perspectives of the organisation or occupation type (Savickas, 2001). Additionally,

occupational mobility within careers has been a focus of economists and others who approach the

issue from the business or management perspective of human capital theory (Dolton & Kidd, 1998),

the labour market, and human resource structures within organisations.

Despite attempts for interdisciplinary collaboration from both vocational psychology

(Collins & Patton, 2009) and management (Arthur, 2008), the career literature remains siloed:

“various paradigms pursuing their own conception of what career research is, without the slightest

feeling of responsibility to connect their views to other researchers allegedly in the same field”

(Schein, 2007, p. 573).

1.2.1 Lack of definitional precision. The various disciplines in the career literature don’t

share an understanding of key terminology (Arthur, 2008; Baruch et al., 2015; Collin, 1998). In

fact, Lewis and Thomas (1987, p. 183) believe that the way concepts, particularly the terms ‘career’

and ‘mobility’, are used is sufficiently varied to “undermine the validity of the empirical literature”.

Confusingly, definitions are often not objective but rather reflect a particular model or theoretical

position on the issue, so that appreciation of how proposed career models work in practice and the

19

circumstances to which they apply is made difficult (Arthur, 2008). In illustration, in the

occupational sociology literature, career was defined as a sequence of jobs that were progressively

higher ranked positions, with any divergence from an upwardly directed path being considered a

disordered or disrupted path (Spilerman, 1977). This notion of an apparently favoured upwardly

directed linear progression is very different from the ‘lifestyle’ concept of career currently

supported by several disciplines, for example, vocational psychology (see later under definition of

career). From the earlier focus on job sequences, studies of mobility examined reasons for

movements between jobs (Hachen, 1990; Rosenbaum, 1979) with little real focus on occupational

change.

Vague and inconsistent terminology can also mask differences in assumptions about the

nature of ‘occupational mobility’ which can affect the generalisability of theoretical models. For

example, Sicherman and Galor’s (1990) theory of career mobility applies only where successive

occupations share a skills-base. In comparison, most psychologically based research does not

usually specify the skills-based nature of occupational mobility. Therefore, in examining the

literature care has been taken to ascertain assumptions about the nature of occupational mobility.

1.2.2 Terminology. To overcome ambiguity and to ensure consistent use of terminology, it

is important to nominate definitions to be used in this research (Collins & O’Cathain, 2009).

Specifically, definitions for knowledge and skill-sets, jobs and occupations, career, career structure

and occupational mobility will now be considered.

Knowledge and skill-sets. Knowledge and skills are usually focussed on a particular body of

knowledge, and are together the product of higher education and training (Elias & Purcell, 2013).

Two types of knowledge and skill sets are required for most occupations: occupationally-specific

knowledge and skills-sets and, transferable, core, or generic skills- sets that are transferable across

different occupations (Bennett, 2002; Cox & David, 2006; Dench, 1997; Dept of Education Science

and Training & Ageing, 2002; Donnelly, 1994; Nabi, 2003; Zinser, 2003). Specific-skills-and-

knowledge sets are obtained from qualifications that are mandatory requirements (Nabi, 2003) of

particular occupations and include specialised knowledge and analytical tools (Donnelly, 1994).

While there are many descriptions of transferable skills, Zinser (2003) found approximately 80

percent agreement internationally that transferable skills include academic skills such as analytical,

organisational and conceptual skills, problem solving, decision-making (Zinser, 2003), personal

skills such as good communication, ability to work in teams, self-reliance, and adaptability (Nabi,

2003), tolerance of diversity, as well as skills in numeracy and in use of computers (Bennett, 2002).

Following research on Australian businesses, The Australian Chamber of Commerce and Industry

(2002) includes all these as desirable employability skills for Australian industry. Though

20

employability skills are important, and improvement in these may aid mobility and career

development at lower occupational levels, overstressing their importance could perhaps lead to an

oversimplification of the ease of occupational mobility in knowledge occupations. For example,

from a survey of employers, Carson and Kerr (2005) found that in a study with a range of levels and

fields of work, employers emphasised that employability skills were at least as important as specific

skills, but in actual interviews, greater weight was placed on specific skills. As Elias and Purcell

(2013) point out, the concept of ‘employability skills’ initially referred to “basic practical skills

provided to educationally–challenged young people” (p. 3), and are not appropriate material for a

higher education curriculum.

In the present study ‘up-skilling’ (Hain & Denham, 2007; McGuinness & Wooden, 2007)

will be used to mean upgrading specific skills in a particular field in response to technological or

knowledge change. A new term, ‘add-skilling’, will be used to indicate when new skills and

knowledge are added to an original set of specific skills, and the term ‘change-skilling’ or ‘re-

skilling’ will refer to acquiring a new set of specific skills and knowledge such that the original set

are not relevant in the changed occupations.

Job and occupation. For the purposes of this study, a job is “a particular kind of work with

a particular employer” (Rosenfeld, 1992, p. 40). An occupation “is made up of a constellation of

specific requisite skills, knowledge, and duties that differentiate it from other occupations, and

typically, is transferable across settings” (Lee, Carswell, & Allen, 2000, p. 800). Therefore the

difference between a job and an occupation is that a job is employer and time specific, whereas an

occupation is transferable, so that an individual could move between several jobs but remain in the

same occupation.

Career. Occupation, profession and career have been used somewhat interchangeably in

the career literature (Lee et al., 2000, p. 800) with some theorists referring to an individual’s

multiple careers or to a change in occupation as a career change (Greller & Stroh, 1995; Muja &

Appelbaum, 2014b; Neal, 1999). Other authors prefer a ‘lifestyle’ concept of career that involves a

sequence of all activities lifelong and includes learning, work and leisure (Adamson, Doherty, &

Viney, 1998; Patton & McMahon, 2014; Savickas et al., 2009; Sullivan & Baruch, 2009; Super,

1980a). To avoid ambiguity for the purposes of the present research, career will be defined in the

context of the world of work (Arthur, 1994; Briscoe & Hall, 2006a; Cheramine, Sturman, & Walsh,

2007; Kerno, 2007; Robbins, 1978; Rosenfeld, 1992; Shamir & Arthur, 1989; Sullivan & Arthur,

2006; Super, 1957), as the sequence of jobs and occupations throughout a person’s life. Thus career

will be considered to be a ‘whole-of-working-life’ concept in which there may be mobility in the

21

form of job changes, occupational transitions and possibly actual skills-set changes from one

occupation to another.

Occupational mobility. Given the focus on the nature of occupational mobility in this

research, it is important to distinguish between different types of skills-and-knowledge-based

mobility. Three knowledge and skill-based levels of mobility are relevant here. First, changing

jobs or job mobility will be used to refer to a change of employer, that is, a much more

straightforward change than changing occupations (Longhi & Brynin, 2010) which requires the

acquisition of new skills and/or knowledge (Feldman & Ng, 2007). Second, movement between

occupations that is not part of a typical career progression, but where the two occupations share a

quantity of specific skills and knowledge enabling transferability of some expertise will be labelled

‘occupational transition’. The ease of occupational transition depends on the degree of

transferability of specific skills and knowledge (Shaw, 1987). Third, the term ‘disjunctive

occupational change’ will be used to refer to the type of change when a person moves from

occupation A to occupation B such that skills specific to occupation A are of little or no value in

occupation B (Neal, 1999). The study by Carless and Arnup (2011) is one of the few on

occupational change using a similar definition.

1.3 Assessing Occupational Mobility

Several studies have acknowledged the difficulties in comparing measures of levels of

occupational mobility obtained over time because of inconsistencies in methodology, data

collection procedures, and coding processes (Kambourov & Manovskii, 2008; Parrado et al., 2007).

As an example, comparing an individual’s occupations over time was based on self-reported

descriptions of his/her occupations which tended to inadvertently vary over time resulting in

misclassifications and thus overestimations of the amount of change, as can happen when self-

report data is used (Perales, 2014).

Relatedly, occupational changes “can number from dozens to thousands depending on the

level of disaggregation” used (Moscarini & Thomsson, 2007, p. 808). Further, for accuracy of

mobility estimates in longitudinal studies, Parrado et al. (2007) highlight the need to control for

possible inconsistencies between new and older coding systems.

Controlling for sources of error effectively produces more accurate indications of the true

level and nature of mobility. For example, though Parrado et al. found an increase in mobility, “90

percent of the changes were between occupations or industries that required similar skills” (Parrado

et al., 2007, p. 443); that is, with high levels of skills and knowledge transferability. As this

distinction between add and re-skilling has important implications for the career development

22

pathway options for people in knowledge occupations and is a focus of the present research, the

study has been designed to avoid such sources of error.

1.3.1 Implications of mobility options for university aspirants. The skills-and-

knowledge-based nature of mobility practically available between knowledge occupations and,

importantly, the ease with which such mobility is continuously attainable, determine the career

trajectory options for individuals who initially choose to invest in a university qualification. The

career decision to study for a degree, and then to choose a field of study, often immediately post

school, can be an important life event (Lancaster, Rudolph, Perkins, & Patten, 1999; Vignoli, 2015)

considering the time, cost and commitment required. It is in the context of what is broadcasted as a

time of rapid change and increased occupational mobility, occupational obsolescence and creation,

mass tertiary education and competition for graduate positions (Finkel, 2016), that university

aspirants currently have to make their decisions. This change narrative is readily propagated in the

media and in the political sphere as in the following examples:

“Will YOUR job still exist in 2025? New report warns 50 per cent of occupations

will be redundant in 11 years’ time” (Awford, 2014).

“More than half of students chasing dying careers, report warns” (Brown, 2015).

“World Economic Forum warns of major changes to workplace in ‘fourth industrial

revolution’.” (Craw, 2016).

“Computers could replace five million Australian jobs within two decades”

(Guardian, 2015).

“Young people don't have the skills for future jobs”(Owen, 2016).

The evidence base for these rather dramatic headlines is often not explained, and the

statements can be in conflict with actual census figures and future job prospects calculated by

government statistical bureaus such as noted earlier in this chapter. The forecast of highly mobile

‘new careers’ in an economy characterised by high growth in knowledge occupations in which

mobility is usually low (Sweet, 2011), is a real conundrum. These conflicting contentions of the

future employment situations have the potential to induce uncertainty and insecurity in the

community and in career decision makers contemplating high investment in tertiary education.

Unfortunately understanding the nature and extent of the occupational mobility of

individuals who initially choose knowledge occupations has been blurred due to several factors:

poor multidisciplinary co-ordination with definitions and terminology, methodological problems in

measuring the nature and extent of mobility in the work force, and little empirical investigation,

particularly with regard to specific fields of occupation.

23

1.4 Rationale of the Research

Numerous theories and models of career development assume the availability of mobility as

the change mechanism, but fail to define the nature and extent of that mobility. This neglect raises

the question of the validity and generalizability of the models since studies suggest mobility differs,

for example, between occupational groups. Rather than focusing on a specific theoretical model,

this research focuses on a process: mobility; that in some form features in most theories. The nature

and complexity of the types of occupational changes future knowledge workers can reasonably

expect to be practicably available from an initial choice to study at university is particularly

important from two perspectives: the necessary initial financial and time commitments of university

study (Robertson & Symons, 1990) and the lifetime expectations of earnings levels established as

motivation for choosing university study (Elias & Purcell, 2013; Willis & Rosen, 1979).

Though occupational mobility is frequently discussed in the context of self-constructed

career paths or more generally as an integral part of career development, the skills-and-knowledge-

based nature and extent of the change, or of how difficult change can be to actually achieve, has

been the subject of only a small number of studies (Neapolitan, 1980; Robbins, 1978). Very little

research on actual occupational change in knowledge occupations has been undertaken, especially

with respect to a range of fields of study (Robst, 2008).

Therefore the present research investigates levels and patterns of possible occupational

mobility in knowledge occupations across a range of specific fields of occupation. The focus will

be on individuals who have at least an Australian Qualification Framework (AQF) level 7 university

qualification, that is a Bachelor Degree (Department of Employment Education and Workplace

Relations, 2011), and who have more recently completed another university qualification.

To reiterate, for the purposes of this study, and as field of study is an indicator of field of

employment, the field of further study of individuals who already have at least a Bachelor’s level

qualification will be used as an indicator of future occupational intention. This skills-and-

knowledge-based approach both enables an empirical comparison of fields of study for each

individual, and avoids the difficulties described earlier with self-reported occupational change data

in assessing mobility.

1.5 Chapter Summary

From the current globalisation and rapid technical and social change narrative, lifelong

occupational mobility as a future career pattern is frequently predicted (indeed almost assumed) in

the career theories and models, despite limited empirical validation. There is little specificity about

the nature of the mobility, and changes in the method of work delivery are often not distinguished

from changes in skills and knowledge-sets. Further, limited interdisciplinary co-operation, varied

24

definitions and inconsistent use of terminology confounded by measurement problems, have

resulted in a blurred conceptualisation of the nature of mobility and mobility levels particularly with

respect to knowledge occupations. The influence of demographic and contextual issues and the

realities of the world of work in relation to change in knowledge occupations also require

clarification. Of interest in this study are the nature, ease and extent of mobility in knowledge

occupations which require high initial investments in tertiary qualifications. In practice,

understanding the type and availability of mobility options is particularly important for university

aspirants and to inform career guidance.

Chapter 2 examines the nature and extent of mobility in the world of work from historical

and theoretical perspectives in relation to knowledge occupations, and in the context of the

predicted responses to globalisation and rapid technological change. The processes of change are

also considered along with the influence of age, gender, and cost on decisions to undertake further

tertiary study. Chapter 2 concludes with the research aims and questions. Chapter 3, the methods

chapter, explains the design of the study, data used, methods of analysis, sources of error, and

ethical issues. Chapters 4, 5, 6, and 7 present the results of the quantitative and qualitative phases

of the study. In Chapter 8, the results of both phases are discussed and the conclusions and

implications presented in Chapter 9 complete the thesis.

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Chapter 2

Occupation Mobility and Change in Knowledge Occupations

This chapter follows from the introduction to the research problem in the previous chapter.

The literature is reviewed regarding the concept of mobility in the world of work in the contexts of

the alleged “era of mobility” (Gubler et al., 2014b, p. 642), theories and models of career and the

related ‘new’ careers of highly mobile, knowledge workers in a rapidly changing world of work.

Firstly, unique features of the nature of knowledge occupations are described to

contextualise the mobility discussion in the occupational level of interest in the research. Secondly,

an exploration of the many-faceted concept of mobility is approached from historical and

theoretical perspectives, to identify the different aspects of job and employment change included in

the concept, and to describe proffered models of mobility and consider critiques. The mobility

discussion then considers the current debate on the notion of a dominant mobility discourse and

change narratives, with their potential to influence university aspirants career decision-making.

Skills-and-knowledge-based mobility in knowledge occupations is then examined from an

economic perspective, including human capital theory and the role of skills transferability. Related

to skills transferability, the relationship between initial field choice and future mobility options is

examined followed by the related debate on the benefits of initially choosing specific or generic

type degrees.

Next, age, gender, the cost of education, and course availability, seen as impacting

occupational mobility, are considered in relation to mobility in knowledge occupations. Finally,

aspects of personal motivation for occupational mobility and change are presented with discussion

of the two most popular fields for second-study. The chapter concludes by presenting the aims of

the research and the research questions which have been developed to address the research problem

and informed by the literature review.

The following section presents characteristics of knowledge occupations which set them

apart as a loose occupation category in relation to the issue of skills-and-knowledge-based

occupational mobility.

2.1 Features of Knowledge Occupations

Occupations have been classified in relation to the high knowledge (graduate)/ low

knowledge (non-graduate) dichotomy, but there is no perfect definition of the distinction (Baldwin

& Beckstead, 2003; Elias & Purcell, 2013). As the present research focussed on mobility in terms

of changes in skills and knowledge-sets, an education-based definition was used such that

knowledge workers are those with university degrees, and knowledge occupations require such

26

qualifications (Paul, 2011). Thus graduate, or knowledge occupations are a heterogeneous group

unique in that they require considerable investment, often upfront, in money (cost of tuition,

foregone salary) and time, to qualify for entry.

Knowledge workers are usually loosely referred to as professionals, and distinguished from

skilled tradespeople by the complex nature of the combination of both high level theoretical

knowledge and practical skills (Smeby, 2012). Generally, knowledge occupations are characterised

by codes of ethics and standards, mandated from within the occupation (Schomburg, 2011).

Continuing professional education and professional development to engage with the ongoing

accumulation of knowledge in the field (Jensen, Lahn, & Nerland, 2012; Smeby, 2012), are usually

mandatory for membership of professional associations, or, in the case of technologically-based

professions such as computer engineering, for keeping up with the ongoing rapid change (Nerland,

2012). Different professions have different knowledge cultures but this continuous up-skilling

increases levels of expertise and specialisation (Nerland, 2012).

Recognised professions such as medicine, psychology, and law are protected by “three

institutionalized strategies of closure—licensing, formal educational credentials, and voluntary

certification” (Weeden, 2002, p. 91). These closure strategies provide the boundaries that have a

limiting effect on entry (Okay-Somerville & Scholarios, 2014) eliminating or limiting competition

between different professions and thus rendering the occupations more secure. The protected nature

of professions is a motivation for occupations to achieve professional status (Neal & Morgan,

2000). Personal competence is valued over organisational experience and identification (Sammarra,

Profili, & Innocenti, 2013). Vocational attachment tends to be with the profession (Mallon, 1999;

Yu, Bretherton, & Schutz, 2012) rather than with an organisation (Lee et al., 2000), so that

knowledge and skills are easily transferable between organisations (Thompson et al., 1968). With

some professions such as many health related fields, and law, private practice is an option, but

private organisations, or the public sector also offer many positions, though the working situations

of professionals tends to vary across cultures and political systems (Donnelly, 2009; Jensen et al.,

2012).

Recognised professions can offer stability of employment. A perhaps common conception

that a profession is a career for life, contradicts the mobility discourse and is reflected in a comment

by Borghi, Mainardes, and Silva (2016, p. 181) following research on expectations of Brazilian

university students: “… after all, students enter an undergraduate course to acquire a profession that

will benefit them for their entire lives”. However, such professions can also mean early ceilings on

career development (Thompson et al., 1968) resulting in flat rather than upward career trajectories.

Mobility is usually horizontal between jobs at level, to closely related occupations with extra

27

credentialing (Yu et al., 2012), or alternatively, vertical into specialisation, administration or senior

management. For Defillippi and Arthur (1994), professions provide occupational identity as an

alternative to bureaucratic careers, but offer the flexibility of boundaryless type careers.

Paradoxically then, recognised professions could be described as bounded occupations (King,

Burke, & Pemberton, 2005) as well as boundaryless careers (Arthur, 1994; Arthur & Rousseau.

1996).

In contrast, with knowledge occupations outside the recognised professions, for example

some entrepreneurial (Chan et al., 2012) and managerial professionals, proactive personality and

leadership qualities can enable suitably credentialed individuals to differentiate themselves in

organisations (Uy, Chan, Sam, Ho, & Chernyshenko, 2015). Graduate entry also promises a high

career trajectory into senior management roles, often based on generic skills such as communication

abilities, problem solving and general intelligence (Yu et al., 2012).

Because of their proposed importance in the knowledge economy (Jensen et al., 2012), and

the required initial investment of money and time, information on future mobility/career pathway

options following graduation is important for university aspirants. Next, workforce mobility will be

considered in relation to knowledge occupations.

2.2 Mobility: A Generic Term with Many Aspects

The present time has been described as an “era of mobility” (Gubler et al., 2014b, p. 642);

where new, very mobile careers of the future are developing. Yet unpacking the meaning of such

descriptions and predictions is complicated by inconsistent use of terminology as discussed in

Chapter 1. A tendency not to distinguish between different types of mobility, arguably produces

confusion about the concept, and could inflate impressions about the magnitude of any of the

various types of mobility in career models. In different studies, the concept of mobility has

incorporated not only changes in jobs or occupations (e.g., Lyons et al., 2015; Moscarini &

Thomsson, 2007; Muja & Appelbaum, 2014a), but also in the way the skills and knowledge are

delivered (e.g., Clarke, 2013; Duberley, Mallon, & Cohen, 2006; Mallon, 1999; Mallon & Cohen,

2001).

Many changes in the way skills and knowledge are delivered at work have been described

including subcontracting, contingent work (Cappelli, 1995), portfolio careers, consultancies, fixed

contracts, self -employment (Duberley et al., 2006; Mallon, 1999) and geographical relocation

(Dolton & Kidd, 1998; Stroh, Brett, & Reilly, 1994; Sullivan & Arthur, 2006; Thompson et al.,

1968). Professionals forced out of organisations via restructuring and redundancies are more likely

to become independent contractors (Sterret, 1999), where they are basically self-employed and

tender for packages of work. The term continuity-in-change has been used to describe a move from

28

employment in a public utility to performing the same tasks from a contract type position, where the

change is not in occupational field but in the method of delivery (Dries, Pepermans, & Carlier,

2008).

2.2.1 The nature of occupational mobility from a historical perspective. Over time, there

have been changes in the focus of mobility. Examination of different conceptions of occupational

mobility from a historical perspective provides some insight into current confusion about the

mobility question. In earlier studies, occupational mobility was defined in terms of occupational

stratification within an organisation or profession, and was measured in terms of occupational status

changes of individuals (mostly males) (Reiss, 1955; Rosenbaum, 1979). Intergenerational

comparisons also appeared to be of particular interest, that is, how the occupational status of a son

compared with that of his father (Form & Miller, 1949; Reiss, 1955; Warner & Abegglen, 1955).

Other types of mobility measured included interfirm mobility, geographical mobility (Warner &

Abegglen, 1955) and intra-organisational mobility patterns (Martin & Strauss, 1956; Rosenbaum,

1979).

The centrality of occupational stratification in the concept of mobility, which was the basis

of the articles mentioned above, was prevalent in the early to middle twentieth century attitude that

individuals’ job shifts were expected to conform to normal or orderly upward paths (Reiss, 1955;

Warner & Abegglen, 1955). Some considered that movements away from such orderly upward

paths indicated disordered careers, possibly with serious negative consequences not only for

individuals but also for the society and the economy, especially if too many people exhibited such

patterns (Thompson et al., 1968; Wilensky, 1961b).

Much of the earlier research also concentrated on job mobility within organisations (e.g.,

Rosenbaum, 1979; Spilerman, 1977) in keeping with the traditional model of organisationally based

careers (Arthur & Rousseau, 1996a; Hall & Mirvis, 1995). Later studies focussed on job mobility

within and between employers (Hachen, 1990; Rosenfeld, 1992; Rosenfeld & Jones, 1986; Topel &

Ward, 1992), and on intra- and inter-organisational shifts between both jobs and occupations

(Valcour & Tolbert, 2003). Over the past 20 years, a narrative of rapid technological change and

globalisation has seeded the proposition of a “new era” in the nature of careers with predicted large

increases in mobility (Defillippi & Arthur, 1994, p. 311), incorporated in new models of career

development. The resulting renewed interest in workforce mobility research is welcomed generally

but more attention to differentiation between types of mobility is required (Donnelly, 2009; Inkson,

Ganesh, Roper, & Gunz, 2010). The role of changes in organisations in influencing mobility has

been proposed (Arthur, 1994; Arthur & Rousseau, 1996a; Clarke, 2013)

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2.2.2 Organisational change. Delayering, downsizing and outsourcing in response to

global economic realities (Betz, 2003) is seen by some to have diminished the opportunity for the

traditional, upwardly mobile careers defined within organisations (Baruch, 2004; Clarke, 2007;

Defillippi & Arthur, 1994; Sterret, 1999). Therefore it is predicted that greater inter-organisational

mobility will increasingly become a feature of career patterns in the future (Arthur, Khapova, &

Wilderom, 2005; Valcour & Tolbert, 2003), though there is some evidence against this vision

(Chudzikowski, 2012; Clarke, 2009; Kovalenko & Mortelmans, 2016). Thus the proposed effects

of these perceived organisational changes have become the subject of a debate which focuses on the

relative extent of organisationally-focused or traditional career patterns reminiscent of those

orderly, stratified paths of earlier mobility studies, as opposed to self-managed or increasingly

mobile careers (Arthur, 2014). The nature of occupational mobility options is one of the issues at

the heart of this debate which features mainly three models of career: the traditional model (Arthur

& Rousseau, 1996a), the boundaryless model (Arthur, 2014; Arthur, 1994) and protean careers

(Hall & Mirvis, 1995).

2.2.3 The traditional model. Wilensky’s (1961a) view of career as “a succession of

related jobs, arranged in a hierarchy of prestige, through which persons move in an ordered (more

or less predictable) sequence” (p. 523) contains most of the elements of the traditional model. In

the traditional model, individuals enter an organisation, perhaps with an initial set of specific skills,

and progress up the promotional ladder in a linear, ordered sequence of movement via a series of

related occupations, continually building on the initial skills-set. The organisation is seen as

directing or managing an employee’s ‘career’ and providing the appropriate training or education

for progression. Thus the traditional career structure cannot exist outside a large organisation, and

the implicit assumption of transferability of some specific skills is paramount. It follows then that

the initial choice of field of occupation may be important in determining an individual’s future

career development options.

2.2.4 Self-managed, mobility focused career models. Arthur (1994, p. 303) proposed the

boundaryless career as an “inter-organizational concept”, and Hall and Mirvis (1995) described

protean careers, both of which feature individually driven potentially continuous mobility. These

approaches have been a major focus of the career literature from the management perspective,

centring on increased mobility managed by individual agency.

The boundaryless career. In keeping with human resources changes in organizations

(Arthur, 1994), individuals may choose not to be bound to one organisation, but to move between

organisations. To maintain employability in a boundaryless career (Arthur, 1994; Arthur &

Rousseau, 1996a), inter-organisationally mobile individuals seek to develop a range of marketable

30

specific-skills, given the demise of employment security (Duberley et al., 2006). Such skills can

take the form of “post qualification know-how accumulation” (Defillippi & Arthur, 1994, p. 131),

or ongoing up-skilling and add-skilling which individuals engage in via their professional

association or individually (Bird, 1994), and that show increased commitment to their occupational

groups (Lee et al., 2000). Consequently it could be argued that the time and ongoing cost required

to develop competitive expertise would render it increasingly difficult for individuals to change

between occupational groups at the same level. Some may move into management roles by add-

skilling.

The protean career. The Protean career differs from the boundaryless model in that it refers

more to career orientation than actual trajectory (Forrier, Sels, & Stynen, 2009), and describes how

individuals adapt to developing a career unbounded by organisational culture and restraint (Hall &

Mirvis, 1995). The Protean career views individuals as life/career managers responsible for

choosing their work and learning activities throughout their lifespan. Individual–instigated

“continuous learning is required for continued success” (Hall & Mirvis, 1995, p. 276). Hall and

Mirvis postulate a series of many shorter learning cycles over the life span in response to changing

skill demands in an individual’s occupation, and thus continuous adaptation. With the emphasis on

continual learning (up-skilling or add-skilling) as opposed to retraining, the protean career concept

does not seem to address an option of disjunctive occupational change.

Aspects of individual agency. The boundaryless and protean careers are “essentially viewed

as the product of individual agency” (Rodrigues et al., 2015, p. 3). As such, in the new post

corporate era (Currie, Tempest, & Starkey, 2006), individuals are required to design and self-

manage their career lifelong, moving between different organisations (boundaryless model) and

making constant small adjustments to their skill sets to maintain employability and for career

development (protean model). However, the functioning of such models implies the ongoing

availability and ease of mobility (Borghans & Golsteyn, 2007), and overestimates the effectiveness

of agency as opposed to the many other contextual factors that influence employment outcomes

(Inkson et al., 2012).

In summary, these three career patterns appear to assume that occupational mobility is either

intra-organisational upward movement between related occupations (traditional model) or between

organisations but in related occupations (boundaryless model), or is achieved by constant small

changes (protean career). Both the boundaryless and protean models predict increased mobility and

a general decline in traditional careers, and proponents discuss the world of work in terms of an

organisational (bounded)/ non-organisational (unbounded) dichotomy, stressing the ongoing demise

of corporations as large employers (Arthur & Rousseau, 1996a; Tams & Arthur, 2010). Therefore

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the future forecasts for career development include increased, self-directed, ongoing mobility for

individuals in the workforce. The next section presents critical evaluations of the boundaryless

model and of the assumed increasing demise of traditional careers.

Critiques of the boundaryless and protean models. The boundaryless model of career has

been criticised on several aspects in relation to contemporary careers: increased mobility between

employers is not supported by empirical evidence (Rodrigues & Guest, 2010); job tenure tends to be

stable in Europe, Japan and the USA (Borghans & Golsteyn, 2012); careers have multiple, limiting,

boundaries, and boundaries define occupations (King et al., 2005); the models lack accuracy

(Inkson, 2006); too much emphasis is placed on the power of individual agency (Okay-Somerville

& Scholarios, 2014), neglecting contextual and economic constraints (Inkson et al., 2012); and

career development behaviour varies with demographics, context, industry, culture, legislature, the

labour market and the economic situation (Leach, 2015; Mayrhofer, 2012; Okay-Somerville &

Scholarios, 2014; Purcell et al., 2007; Wilton, 2011; Zacher, 2014). These criticisms broadly focus

on some empirical evidence, cultural differences, vagueness /inconsistency, and a tendency by

proponents to ignore possible negative outcomes associated with boundaryless model. Each of

these is discussed in more detail below.

Empirical studies of mobility. Recent empirical studies from Portugal, the UK (Rodrigues &

Guest, 2010), Germany (Kattenbach et al., 2014) and Austria (Chudzikowski, 2012), have

questioned whether the rate of mobility has changed in Europe. Importantly, traditional careers

among business graduates are “far from dead” (Chudzikowski, 2012, p. 304) and it is possible that

old and new career types can simultaneously exist in the same organisation to the benefit of both

(Sommerlund & Boutaiba, 2007).

There is also no evidence of strong increases in career complexity among the careers of

German workers in recent decades, with little influence of economic globalisation and industry

growth (Biemann, Fasang, & Grunow, 2011). In contrast to the boundaryless and protean models,

there is evidence of large decreases in average career mobility internationally with increasing age

(Inkson et al., 2012). The mobility models have also been criticised as incompatible with the usual

career structures in some industries, including the sports industry (Minten & Forsyth, 2014). Thus

the demise of the traditional career is generally not supported by evidence (Lyons et al., 2015).

Even in Australia, the Australian Bureau of Statistics figures (Australian Bureau of Statistics, 2010),

suggest little recent change in job and occupational mobility.

Culturally based critiques. The idea of the boundaryless career was proposed by American

theorists (Arthur, 1994) and some of the criticisms of the boundaryless model appear culturally

based. It has been contended that the focus on increasing mobility and individual agency in shaping

32

careers reflects economic, cultural, legislative, social and psychological aspects of the USA (Gerber

et al., 2009) making it psychologically and institutionally compatible with the American corporate

world (Briscoe, Hall, & Frautschy DeMuth, 2006; David Thomas & Inkson, 2007), and thus a US

corporate world phenomenon (Forrier et al., 2009; Kattenbach et al., 2014).

As Germany and France have different institutional, legislative and educational

environments from the USA, the orientation and structure of the boundaryless and protean models

have only marginal application to European labour markets (Dany, Mallon, & Arthur, 2003;

Kattenbach et al., 2014). For example, the rate of job mobility in US college graduates significantly

exceeds that in Japan and eleven European countries (Borghans & Golsteyn, 2012), and

longitudinal rates of job stability in countries with different cultures show little change over the last

two decades (Rodrigues & Guest, 2010). Further, national institutions (Donnelly, 2009) and

“cultural differences tend to play a role in people’s motivation towards certain career profiles”

(Segers, Inceoglu, Vloeberghs, Bartram, & Henderickx, 2008, p. 228; Sullivan, Carden, & Martin,

1998; Sultana, 2011). Career concepts developed in Western countries have different meanings in

other cultures; for example, China (Ngo & Li, 2015; Tu, Forret, & Sullivan, 2006), New Zealand

Maori people (Pringle & Mallon, 2003; Reid, 2011), and Russia (Khapova, Arthur, Wilderom, &

Svensson, 2007). In individual nations, such concepts can be dynamically related to changes in

social, political and economic conditions, for example “in Soviet times, during the Russian

transition, in the 1990’s and in Russia today” (Khapova et al., 2007, p. 81). Interestingly, in both

the Russian and Chinese studies the United Kingdom and Australia were grouped with the USA and

classed as purveyors of Western, Anglo Saxon cultural attitudes to career.

Given these many sources of incompatibility of the boundaryless and protean models with

cultures and labour markets outside America, Kattenbach et al. (2014, p. 56) proposed a

“contextualization” (p. 56) of the mobility concept encompassing the country, cultural context

(Stead, 2004; Sultana, 2011), particular professions, industries, organisational characteristics, and

age of the individual. More generally, other contributors have suggested that depicting the world of

work as primarily a dichotomous divide moving towards “the post corporate era of boundaryless

careers”(Currie et al., 2006, p. 755) is simplistic and inaccurate, particularly with knowledge

occupations, because of the variety of work options (Dany, Louvel, & Valette, 2011; Rodrigues et

al., 2015).

Inconsistency. Referenced in over 5,000 articles, the boundaryless model (Arthur, 2014), is

still prominent in the career literature, but has “suffered from conceptual vagueness and

inconsistency, and sometimes enthusiastic but less-than-rigorous adoption” (Inkson et al., 2012, p.

324), as in studies by Sullivan et al. (1998), Uy et al. (2015), and Cai et al. (2015). Maybe

33

controversies have developed from some rather loose use of terminology. For example, in his

initial articles on the genesis of the boundaryless idea, Arthur (1994) refers chiefly to the

bounded/unbounded dichotomy in the world of work, but in later articles, described the physical

mobility aspect of boundarylessness as including much more: “actual movement between jobs,

firms, occupations, (italics added) and countries” (Sullivan & Arthur, 2006, p. 21). Earlier, Miner

and Robinson (1994), in discussing the boundaryless model, had also referred to unbounded

occupations, suggesting that occupational boundaries were changing or dissolving (Stern,

Education, Bailey, & Merritt, 1996, p. 8).

It appears to be this extension of boundarylessness to occupations by the proponents of the

model that is seen by some as the major limitation of the boundaryless model (Rodrigues et al.,

2015). The centrality of boundaries in distinguishing occupations has been rigorously argued

(Currie et al., 2006; Inkson et al., 2010; Inkson et al., 2012; King et al., 2005), particularly in

relation to knowledge occupations requiring specific qualifications. Undertaking occupational

mobility among such occupations must necessarily involve boundary crossing. To better understand

factors involved in changing between knowledge occupations, there has been a call for research

focussed on boundaries of occupations (Inkson et al., 2012) and on strategies for the management of

crossing occupational boundaries (Rodrigues et al., 2015), which would arguably be more difficult

between high skilled occupations.

Negative outcomes from increased mobility. Aside from the above criticisms, many

researchers have appeared to accept the boundaryless and protean models and the new career era

with the accompanying idea of self-managed careers, with little questioning or empirical evaluation

(e.g., Banai & Harry, 2004; Currie et al., 2006; De Vos & Soens, 2008; Kelan & Jones, 2009; Stoltz

& Young, 2013). They also appear to see the resultant paradigm shift in career development as

generally positive. Leach (2015), Clarke (2013), and Inkson et al. (2012) are critical of this

unquestioning acceptance, reasoning that without empirical validation, models can be misleading.

Higher levels of career satisfaction have been measured in organisationally based careers (Dries,

Van Acker, & Verbruggen, 2012).

Problems have also been expressed with the adjustment to boundarylessness and the

accompanying reductions in job security, which can be challenging to many people (Kovalenko &

Mortelmans, 2014) depending on their personality, confidence, communication skills, and self-

marketability (Eby, Butts, & Lockwood, 2003; Mayrhofer et al., 2005). Another practical

disadvantage of increased mobility can be depressed earnings (Currie et al., 2006), particularly in

males, (Valcour & Tolbert, 2003). Modelling has indicated that increased occupational mobility

has led to increased wage inequality in the USA, related to some loss of occupation-specific

34

expertise in each move (Kambourov & Manovskii, 2009). This problem applies more to

individuals moving between knowledge occupations than those in low-skilled employment because

of the levels of expertise required (Kambourov & Manovskii, 2009).

In summary, considering vagaries and absence of empirical validation, Briscoe’s conclusion

(2006) that the boundaryless and protean models are more about attitude to career development and

a proactive approach to career management than a formulation of any particular career structure,

seems warranted. The critical evaluations have pointed to a lack of real world evidence in support

of the demise of the traditional model, or of increased mobility between jobs and occupations.

Further, there are mobility issues involved in career development, particularly between knowledge

occupations, that require clarification: the matter of boundaries in occupations, and the related issue

of occupation security. The following section demonstrates that a future of increased mobility in

the world of work is also envisaged by vocational psychology theorists.

2.2.5 Vocational Psychology: Mobility by Adaption. Psychologically based theories of

career focus on the process of career decision-making and development from a personal,

psychological standpoint (Savickas, 2001). Vocational psychology theories have broadened to

consider career development as a lifespan process whereby people engage in work and learning

across the lifespan, which is suggestive of mobility, even though mobility itself is not necessarily

discussed. The construct used to describe the movement of individuals between occupations is

adaptability.

Career Adaptability. Adaptability refers to the individual’s readiness to continually plan

for and adjust to the work role and to changes in work and working conditions (Savickas, 1997;

Super & Knasel, 1981). For Savickas (1997, 2002), career counsellors could ideally assist

individuals to be constantly reevaluating themselves and their environments: exploring possibilities,

anticipating changes and transitions and thus fortifying themselves against, for example, the trauma

of sudden job loss. Adaptability relates to an attitude of willingness to accept mostly gradual

change and an ability to adopt associated coping behaviours.

Thus increased mobility is seen as a characteristic of the nature of future careers from the

perspectives of the mobility models of career and of Vocational Psychology. The

sociological/organisational aspects of career structure and the subjective process of personal career

decision-making could be considered to complement each other. However, increased mobility per

se and the focus on the boundaryless model in particular in current career thinking have been

described as a discourse (Richardson, 2012), and a powerful one (Inkson et al., 2010). The next

section considers aspects and potential influence of that discourse.

35

2.2.6 The Mobility Discourse. The power of discourses to dominate and perhaps guide

thinking has been discussed by Inkson et al. (2009) and by Richardson (2012). Richardson grouped

the ideas of boundaryless careers and career self-management (individuals taking responsibility for

designing and managing their own careers) as related 21st century “career discourse practices” (p.

93). Similarly, for Inkson et al. (2009), the boundaryless career model “generated a powerful

discourse” (p. 3). They questioned the origin of such discourses and expressed concern about their

ability to influence individuals making their career choices.

Though mobility in the workplace has been a focus of some research in the past, the

introduction of the boundaryless model (Arthur, 1994; Arthur & Rousseau, 1996) seems to have

had a catalytic role in bringing the concept to prominence (Briscoe & Hall, 2006b; Gubler et al.,

2014a; Leach, 2015). The discussion on boundarylessness has been limited to academic circles

(Inkson et al. 2009), but the related proposition of increasing mobility in the workforce and the

changing nature of occupational futures has been popularly adopted by politicians and the media.

Therefore this mobility discourse arguably has the potential to influence thought in the general

community, as Inkson et al. (2009) also point out. In the popular press, as discussed in Chapter 1,

the public are intermittently presented with predictions of far reaching, rapid changes such as

occupational obsolescence, unknown future occupations, and technological advances in the world

of work, making current education outdated.

Predicted increases in mobility. Predicted levels of future mobility, whereby individuals

will choose and move between a succession of jobs across different industry sectors in their

working lives (Jarvis, 2002), have been quantified. Jarvis and Keeley (2003) from a study with the

Alberta Government in Canada, forecast that individuals would have between 12 to 25 jobs in five

industry sectors in their future careers. Similarly, in the Australian context, from the Household,

Income and Labour Dynamics in Australia (HILDA) survey data, McCrindle (2014) estimated that

individuals would have 17 jobs in 5 different careers, with retirement occurring at age 75.

However, survey responses about occupation may lead to an overestimation of job mobility and can

be problematic (Perales, 2014), so it is difficult to rely on these estimates.

Also such statistically based forecasts of high but undefined mobility in the future may be

potentially misleading (Richardson, 2012) as no distinction is reported between jobs and

occupations, skill levels of employment, or the skills-and-knowledge-based relationship between

different occupations or industries. Further, there is no mention of variations in levels of job

changing in relation to age which has been well established. Because the mobility discourse

accompanied by this type of quantification has received media attention , school leavers considering

36

investment in a specific skill programme at university could find such predictions, and those of

some futurists, potentially unsettling.

Futurists’ predictions of mobility may influence career decision-making. For example, in

Australia, business futurist Morris Miselowsly recently predicted in a magazine designed for

parents (Richie, 2016), that today’s children would have 14 jobs in six careers with 60% of work

tasks in 2020 not yet invented. Similarly, Rohit Talwar, a global futurist from the UK at a

convention of school principals (Guardian, 2015), predicted that today’s children could potentially

be required to work until age 100 with up to 40 different jobs; and that between 30% to 80% of

today’s jobs will be obsolete in 10 to 20 years. While these and similar forecasts may have

increased perceptions of the inevitability and perhaps ease of career-long mobility, they too do not

appear to offer definitions of the terms, occupation levels, parameters or assumptions used, or the

skills-based relationships between the ‘careers’ they refer to.

In contrast, there is some countering of these types of predictions with information on jobs

and occupations that will remain (Hoy, 2015). A study based on ABS figures of both population

and job growth predicted that some processes in many knowledge occupations, including non-

STEM occupations, may be altered/enhanced by changes in digital technology, but most

occupations will remain, with possibly increased demand related to population growth (Salt, 2015).

Both these sources add some balance to the future visions.

A further concern in relation to the predictions is that little media attention is paid to

findings that mobility can be costly (Jacobson, LaLonde, & Sullivan, 1993; Lee & Wolpin, 2006),

and hampered by mismatches between skill sets and job vacancies (Bachmann & Burda, 2007; Lee

& Wolpin, 2006). Similarly, Borghans and Golsteyn (2007) expressed the concern that mobility is

not always available. Future predictions include not only increased mobility but also increased

specialisation (Jarvis, 2002). The relationship between the two issues is considered next.

Predicted increases in specialisation. Jarvis’s prediction (2002) of widespread increases in

occupations requiring high skills-levels and ongoing learning, that is, increased specialisation, is

well supported (Baruch, 2004; Defillippi & Arthur, 1994; Robbins, 1978), for the new, knowledge

economy, (Cohen, 2006; Collins & Patton, 2009; Paul, 2011). However, specialisation increases

expertise and reinforces occupational boundaries, thus raising the cost of occupational change

(Rodrigues et al., 2015), so that specialisation in increasing numbers of occupations would lead to

widespread decrease in mobility between occupations (Allen & Van der Velden, 2011; Weiss,

1971). Occupational mobility would be practically difficult to achieve other than between very

similar occupations where a major transfer of skills was possible, or among remaining unskilled

occupations.

37

Further, increased specialisation could also result from reductions in organisational careers

and thus lower job security. Individual career managers may increasingly specialise to remain

competitive and ensure continued employability, rather than change occupations and surrender

human capital (Dolton & Kidd, 1998). For example, within the Indian Administrative Service,

specialisation, in contrast to a diverse work history, leads to promotion (Ferguson & Hasan, 2013).

Individuals are shifting their identification and loyalty from the organisation to their specialised

occupations (Cappelli, 1995; Currie et al., 2006; Mallon, 1999; Stroh et al., 1994; Yu et al., 2012),

and thus to occupational bodies.

Therefore the actual rate of mobility decreases as expertise in specific knowledge and skills

increases. This supports the idea of incompatibility between future concepts of increasing mobility/

increasing specialisation in the knowledge economy, unless the increased mobility envisaged was

between similar or unskilled occupations. Once again, the apparent contradictions may stem from

lack of specificity about the type of mobility being referred to: job mobility or add-skilling for

moving between different organisations, as opposed to occupational mobility. Allen and Van der

Velden (2011), in their REFLEX project studying professional employment in Europe, also point

out this ambiguity between increases in both mobility and specialisation in conceptions of the

knowledge economy.

In summary, the focus on the boundaryless career model (Arthur, 1994; Arthur, 2014) in the

career literature has been labelled a powerful discourse (Inkson, 2009). The predicted extent of

increased mobility and the time frames involved have been quantified by some, but are propagated

without stated specifications, making workforce implications very difficult to determine or analyse.

However, via the popular press, the internet, and other mechanisms, the mobility discourse has

flowed from academia to the community, creating a potential for individuals’ career decision-

making to be influenced by powerful discourse which may not reflect the true situation with

knowledge occupations, which are the focus of this study. Also there appears to be an

incompatibility between predictions of large increases in both mobility and specialisation, unless

the mobility involves changing employers only.

As raised above, in knowledge occupations, the cost of retraining is a factor limiting

changes in skills and knowledge (Weiss, 1971). Hence, an important consideration in the analysis

of mobility options in knowledge occupations is the economic perspective, as discussed next.

2.3 The Economic Perspective

The relationship between occupational choice and the labour market can be considered in

terms of some basic tenets of economics. From a neoclassical economic theory perspective, labour

supply and demand in particular occupations generally fluctuates according to the cost of that

38

labour, and can vary widely over time for a variety of reasons (Cain, 1976; Fleetwood, 2006) such

as the global economic situation, technology changes, and education and training costs. Further, the

cost of education (direct costs and foregone earnings) (OECD, 2015), and physical investment, can

determine occupational mobility (Sommers & Eck, 1977) according to a theory suggested by

Becker (1962). His idea of embedding resources in people, termed investing in human capital, has

become prominent in the economic literature.

2.3.1 Human capital theory. The economic feasibility of occupational mobility depends

not just on the costs of training, but also on the accumulated costs of the change referred to as the

‘opportunity cost’ (Blau et al., 2009; Chesters & Watson, 2013; Neapolitan, 1980; Packard &

Babineau, 2009; Robbins, 1978; Robst, 2008). As it is very difficult to simultaneously qualify for

multiple occupations, or to justify the additional expense of further education, reduced mobility in

knowledge occupations is implicit in human capital theory (Campbell & Banerjee, 2013; Freeman,

1971).

If higher costs of training and investment are associated with decreased levels of

occupational mobility, disjunctive occupational change would predictably be unlikely between

occupations requiring higher levels of education. This raises questions about career path options of

individuals in knowledge occupations.

In human capital theory the positive correlation between level of education and earnings is

well established (Frank & Walters, 2012), and the value of occupationally-specific human capital

has been estimated to be higher for professional-level workers than for other groups (Sullivan,

2010). Including both education and postgraduate time in industry, van der Velden and Allen

(2011) estimated an average of 6.5 years are required to become an expert in professions requiring

bachelor-level qualifications and 8.1 years for professions requiring postgraduate qualifications.

The expertise gained in years of professional practice adds to the accumulation of human capital,

making the cost of changing to another occupation directly dependent on time in the original

occupation. As an example, Pavan (2011, p. 580) estimated that in 10 years “the accumulation of

career-specific human capital accounts for more than 40%” of the salary increase of a worker with

some college education.

Firm-specific human capital anchors people to one organisation, whereas in contrast,

occupation-specific human capital ties people to their occupations, easing inter-organisational job

shifts (Campbell & Banerjee, 2013; Dolton & Kidd, 1998; Kambourov & Manovskii, 2009).

Human capital accrued from a university qualification is related to the major field of study, and

cannot be completely transferred to other occupational fields, so that not working in the field of the

major also incurs a loss of human capital and thus a loss in wages (Robst, 2008). Further, time

39

spent gaining qualifications and higher occupational experience in the wrong occupation also

increases the opportunity cost of switching, because of the loss of the accumulated human capital

(Baird, 2012). These considerations highlight the importance of the initial occupational choice.

Several factors contribute to the opportunity cost of change at any level. These include the

cost of retraining, any loss of salary while retraining, plus remuneration loss from entering another

profession at entry level and thus having to develop expertise to progress in the new field to the

standard attained in the initial occupation.

2.3.2 Transferability of skills and the cost of change. The investment necessary in

changing between knowledge occupations is determined in part by the level of skills and knowledge

match between the occupations (Borghans & Golsteyn, 2007). The skills and knowledge match

determines the transferability of skills between those occupations (Shaw, 1987) such that the more

complete the change in skills and knowledge set necessary, the higher the opportunity cost of

change (Borghans & Golsteyn, 2007). Clearly then there would be greater loss of human capital in

an occupational switch compared to a change of employer or industry (Kambourov & Manovskii’s,

2009b), and the highest cost of change would occur if that change was disjunctive; that is, if there

was little transferability of skills and knowledge between the original and changed occupation.

The level of occupational mobility depends on the transferability of skills, while earning

power depends on the level of specific-skills-and-knowledge accumulation (Weiss, 1971).

Therefore increasing income provides the motivation to continually increase levels of specific

skills, thereby decreasing the chances of transferring to other occupations. In this way, the actual

rate of mobility decreases as expertise in specific-skills-and-knowledge increases (Weiss, 1971).

Parrado et al. (2007) assume that as higher-educated workers have a greater stock of human capital,

they therefore have a wider range of skills to sell. However, Weiss argues that this does not

necessarily apply to specific skills. Parrado et al. did not specify whether they were referring to

specific or non-specific skills.

It follows that occupational mobility should be lowest between specific-skill occupations

with higher entry costs, such as the requirement of a specific tertiary qualification (Elliott &

Lindley, 2006; Farrell, 2009; Greenaway et al., 2000; Mullins, 2009; Sicherman & Galor, 1990).

Therefore the level and skills-based nature of predicted occupational mobility feasible for

individuals who initially chose high initial investment occupations comes into question, particularly

if they wish to maintain income levels and status. For professional groups in the early study by

Form and Miller (1949), the move to professional level was quick and then “few risked trying other

jobs” (p. 322). Similarly, more recently, Parrado et al. (2007) found less mobility in higher

educated workers, but suggested that this reluctance to move is reducing with time.

40

In summary, the cost of changing occupations depends on the similarity of the occupations,

the cost of training and the time necessary to develop expertise. In economic terms, the level of

occupational mobility between occupations is directly related to the opportunity cost of the change.

Therefore the level of transferability of skills and knowledge is a likely determinant of the level of

mobility between occupations, and warrants consideration in models of career. The skills and

knowledge transferability / levels of mobility relationship raises the question of the role of the skills

and knowledge acquired in the initial degree in the determination of future career direction options,

which is discussed next.

2.4 How important is the initial choice?

In applying mobility models of career to the real world of work, it is important to understand

the role of transferability of skills and knowledge included in the modeling. In their economic

model of occupational mobility, Sicherman and Galor (1990) explicitly define career path as “a

series of occupations, characterized by the transferability of skills and experience from one to

another, that form a feasible working career” (p. 173). However, when any assumptions about the

skills-based relationship between successive occupations are not made explicit in model

development (Inkson et al., 2012), it is probable that applications of the model may return

misleading conclusions. Thus the transferability of skills may be assumed rather than explicitly

stated in some mobility research and in mobility models, further adding to confusion concerning the

real nature and extent of mobility predicted.

There appears to be a consensus that ongoing change of some nature will be a feature of

careers in the future (e.g., McMahon, Patton, & Tatham, 2003). The skills-based nature of change in

knowledge occupations implicit in the career models discussed earlier, offers some insight into the

skills-based extent of options for individuals who wish to remain within knowledge occupations.

In the traditional model, individuals progress through the organisational hierarchy by

continually adding skills and knowledge to their initial set. Alternatively, in the boundaryless

model as originally described (Arthur, 1994; Arthur & Rousseau, 1996), individuals would

continually increase expertise in their marketable specific-skills set to remain competitive in the

inter-organisational job market. Though Hall and Mirvis (1995) envisage individuals with a protean

career orientation moving through a number of career cycles lifelong, they also describe learning

for continuous adaptation to keep pace with skills and knowledge changes in the individual’s

occupation, which is similar to Savickas’s concept of life-designing (Savickas et al., 2009). The

implied degree of change in skills and knowledge set incorporated in the protean career idea seems

rather obscure.

41

Thus much of the discussion on workforce mobility and career development points to the

prominence of the initial skills and knowledge set as a basis to be built on in some way, either by

up-skilling or add-skilling. Further, when Super (1980) adjusted his model indicating that the

stages could be revisited, and individuals could retrain to change occupations, he also indicated that

the first occupation was one determinant of later occupational positions open to individuals. Thus

he suggested a continuity of some of the initial specific-skills-and-knowledge set, though the nature

of changes he incorporated in his model was essentially unspecified. Clearly though, he placed

some focus on the importance of the initial occupation.

In addition, if the availability of mobility is primarily related to the transferability of, as

opposed to a disjunctive change in, skills and knowledge-sets, the initial choice would play an

important role in future career direction options. Overall, actual disjunctive change of skills and

knowledge-sets, in knowledge occupations is relatively rare (Blau, 2007). Specific skills-set

changes between high level occupations are referred to as classical transitions (Mayrhofer et al,

2005). There is a consensus among some researchers that such a transition period can be potentially

destabilising and stressful because of factors such as uncertainty (Mayrhofer et al, 2005), the need

for identity reconstruction (Blenkinsopp & Brenda, 2004), and physical realities such as loss of

income, time and expense of retraining and job seeking (Blau, 2007).

In Navarro’s article (2004) on occupational change among physicians, nine of her eleven

cited clients changed out of clinical practice to a related occupation which also required specific

medical knowledge, some adding a Master of Business Administration (MBA) for medical

administration or management. The remaining two went into a business or finance-related

occupation, one doing so via completing an MBA. She does not mention whether or not the

decisions to stay within the medical environment were purely choice related or if other factors such

as the opportunity cost of disjunctive change were involved. Similarly Parrado’s study (2007) of

occupational change in knowledge occupations found that 90% of engineers who made a change

went into like occupations, providing another example of maintenance of the initial skill-set.

Because of the low levels of transferability and thus low mobility between occupations

requiring specific skill-sets, the initial choice of a particular occupationally specific degree would

be more deterministic of degree-related future options than a generic type degree, unless the

individual chose to use the specific skill degree in a generic way in the world of work. In some

companies, graduate recruitment is based on aspects of the individual and the quality of their degree

rather a specific-skills-and-knowledge set the qualification may offer (e.g., McKensey & Company,

2016).

42

Similarly, the emphasis in the economic literature on the career importance of human capital

accumulation and on the transferability of skills in reducing the opportunity cost and thus increasing

the availability of occupational mobility, also suggests the deterministic value of the initial

occupational choice. Further, disjunctive change becomes more difficult and less likely at various

ages, because of age-specific factors such as lost income, lower salaries, time to recoup the cost,

life-stage responsibilities, and ageism in some professional groups, such as law (Sarason, 1977) and

the private finance sector (Egerton, 2001; Purcell et al., 2007).

In summary, albeit perhaps by inference, the academic literature on job and occupational

mobility and change implies that the initial choice of field of study and/or occupation in

determining the availability of lifelong career development options for individuals in knowledge

occupations is particularly important. Despite the centrality of mobility in current theorising,

disjunctive change does not seem to be factored in. In view of the possible importance of the initial

skills and knowledge-set in future career development options in knowledge occupations (discussed

above), the allied issue of the relative benefits of an initial generalist as opposed to a specific skill

qualification is considered in the next section.

2.5 Generalist Verses Specific-skill Degrees

In the debate about the relative advantages of a specific-skill as opposed to a generalist

initial qualification, there is general agreement about the related classification of fields of study.

Vocational or specific-skill qualifications are said to include engineering, law, health, education and

business Studies, whereas humanities, arts, languages and social science are considered less

vocationally targeted and thus generalist or liberal arts type degrees (Pitcher & Purcell, 1998). To

explore the generic/ specific skills debate, the comparative benefits of both skills types are

discussed with reference to labour market entry, career trajectories, and skills transferability.

2.5.1 The career trajectory benefits of generalist vs specialist degrees. “The relative

merits of specialisation versus greater flexibility are still the subject of considerable discussion in

most countries” (Van der Velden & Allen, 2011; p. 24), so that the career trajectory benefits of

generalist as opposed to specialist degrees in the context of the knowledge economy and an era of

mobility are unclear. Specialist degrees are designed for compatibility with the labour market

requirements for particular occupations, and qualifications-related work is thus easier to find on

graduation (Adamuti-Trache, Hawkey, Schuetze, & Glickman, 2006; Drewes & Giles, 2001; Fenesi

& Sana, 2015; Frank & Walters, 2012; Goyder, 2014; Jackson & Seiler, 2013; Roksa & Levey,

2010).

In contrast, graduates from generalist degrees such as humanities and social science degrees

have difficulties entering the labour market (Drewes & Giles, 2001; Jackson & Seiler, 2001; Roksa

43

& Levey, 2010). These difficulties generally result in periods of unemployment (Pitcher & Purcell,

1998) particularly in males (Drewes & Giles, 2001), part-time or overqualified employment

particularly in early career, or further credentialing (Fenesi 2015). However, despite slower and

more difficult labour market entry after generalist degrees, some studies have suggested careers are

more mobile in nature, with resultant lower levels of any specific expertise (Calmand, Frontini, &

Rostan, 2011) but that there can be a catch- up with eventually steeper occupational trajectories than

with many early ceiling specific skill qualifications (Markey & Parks, 1989; Roksa &Levey, 2010).

Generalist degrees may also return higher salaries and lower unemployment from age 45 (Drewes &

Giles 2001).

2.5.2 Skills transferability and the generic/specific degree debate. From the economic

perspective, the cost of occupational transition is directly related to the transferability of skills and

knowledge between occupations. There is generally less availability of skills transfer between

specific skill occupations depending on the particular pair of occupations, so the high opportunity

cost of change would render occupational transition and disjunctive change predictably difficult

(Borghan & Golsteynl, 2007). In comparison, generalist degree graduates are more likely to be

working in non-degree related positions where their general skills are more transferable, so the

opportunity cost of change is lower and their level of mobility higher (Robst, 2007). Drewes and

Giles (2001) also attributed the later benefits of generalist degrees to the enduring nature and high

transferability of generic type skills. Shniper (2005) supported Robst (2007) and Drewes and

Giles’s (2001) predictions empirically, finding the lowest mobility rates in high specific-skill fields

such as health care practitioners, and technical occupations, architects, engineers, and legal

occupations. Goyder’s (2014) empirical findings, however, did not support the idea that the lowest

mobility was in specific skill fields, but he attributed his different results to the residual effects of

the Global Financial Crisis around 2008.

2.5.3 Questioning the functionality of a liberal arts qualification. It is possible that a

general perception of positive outcomes may be what is attracting more students to degrees offering

generalist skills (Roksa & Levey 2010). Alternatively, in a Canadian study, Fenesi (2015) argued

that the current high enrolments in humanities in Canadian universities are more likely to indicate

that students were ill informed about potential course outcomes.

In Australia, two scoping studies have been conducted since 2001 assessing the

functionality of the Bachelor of Arts (BA) in the context of an increased emphasis on employability

outcomes from higher education (Gannaway, 2010). One university had discontinued the BA and

Social science studies because of the poor entrance scores of students applying, a high attrition rate,

and poor labour market outcomes (Gannaway, 2010). In her 2015 study, Gannaway suggested that

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the very flexible liberal arts type degree in its historic, traditional form was evolving into several

more structured, more labour market oriented qualifications, still all called BA, in response to

student and economic demands (Gannaway, 2015).

On the other hand, Malamud (2011) described a particular advantage of initial generalist

degrees. Students who were required to specialise early in their degree were more likely to change

to an unrelated field, despite the cost, than those in a system that allowed later specialisation. In the

latter situation, students had more opportunity to identify a profession that best matched their

abilities and interests.

In summary, the initial choice for university aspirants appears to be between a generic or a

specific-skills degree. Specific-skills qualifications are likely to lead to easier labour market entry,

perhaps registration, and thus higher job security, though possibly an early ceiling and expensive

change options at level. Alternatively, with a generic type degree, labour market entry can be

difficult, resulting in overqualified or part-time employment on lower wages, or unemployment.

However, mobility options are higher, career trajectories may eventually be higher, and

unemployment in midlife is less likely (Drewes & Giles 2001). Of course, as with many of

Robbins’s participants (1978), there are also the options of alternatives outside of the higher-level

occupation sphere, such as developing a retail type business. The role of the initial choice and the

lower levels of mobility available in knowledge occupations, particularly those requiring a specific-

skills-and-knowledge set, also have implications for the almost populist notion of lifelong learning,

often presented as a solution to many aspects of technical, economic and legislative changes.

2.6 Lifelong Learning: A Mechanism of Occupational change?

“Education provides individuals with human capital, which subsequently raises their future

earnings” (Sicherman & Galor, 1990, p. 172). Continuing to learn and train, called lifelong learning,

assumes, and is an essential feature of, the predicted future new career patterns of ongoing

occupational mobility and change (Harris & Ramos, 2013; 2003; McMahon et al., 2003). Lifelong

learning has become a politicised concept (Boeren et al., 2010) “proclaimed as an instrument for

achieving stronger economic growth, more competition and greater social cohesion” (p. 58), at least

in the European Union. It has been ubiquitously applied to any post-compulsory learning activity

(Boeren, 2009), with unfortunately, no consistency in definition (Axford & Moyes, 2003), and is

variously considered as continual add-skilling in a particular field (Aspin & Chapman, 2000; Field,

2001; Griffin, 1999; Hewett, 2002; Jochems & Koper, 2005), personal development (Arthur, 1994;

Hewett, 2002) or up-skilling in adjustment to technological change or to change in labour force

demand (Karmel & Woods, 2004; Reeson, Mason, Sanderson, Bratanova, Hajkowicz, 2016 ) or

continuing professional development (CPD) (Collin, Van der Heijden, & Lewis, 2012). Again,

45

there remains some ambiguity about the nature and resulting outcomes of lifelong learning in

relation to occupational movement in knowledge occupations.

In reality, there appears to be a concentration on continual learning, that is up-skilling and

add-skilling (Gibb & Walker, 2011) rather than change-skilling or retraining, which is hardly

mentioned (Boeren, 2011; Greenaway et al., 2000), again supporting Blau’s findings (2007) that

actual change-skilling is rare. Sicherman and Galor (1990) also stress up- or add-skilling from an

occupation of origin. Arthur (1994) argued that continuing to learn throughout life increases an

individual’s employability, but he was referring to learning within a network of colleagues, and thus

up-skilling not change-skilling. As mentioned earlier, such continuous up-skilling could result in

groups of very highly skilled professionals whose skill levels would be difficult for a new (second

or third occupation) entrant to attain in a reasonable time-frame and at reasonable cost. Thus in

view of theoretical and practical issues discussed in this literature review, though lifelong learning

is possible, the usefulness of the concept as a generic mechanism of ongoing occupational change,

is questionable. . As in Reeson et al (2016), lifelong learning would perhaps be applicable with

lower level and shorter courses as taught in Technical and Further Education (TAFE) where

individuals could upgrade their computer skills as technology changes, or complete, say, an AQF

level 3 course to re-skill for lower level occupations. The practicability of undertaking such a

career structure is also related to the personal situation of the individual.

2.7 Person-in-Situation Factors Influencing Occupational Mobility

In examining occupational mobility and career development, consideration must also be

given to contextual and other factors that can limit the range of occupational choices practically

available to individuals. In individual occupational choice, the importance of time, age, gender, and

context have been highlighted (Coelli, Domenico, & Zakirova, 2012; Fehring & Herring, 2012;

Fritzsche & Marcus, 2013; Greller & Richtermeyer, 2006; Purcell et al., 2007). Further

occupational transitions in adulthood (later life) are more complex than in the earlier decades of

individuals’ lives particularly in relation to cost, complex life circumstances, family needs,

opinions, responsibilities, and geographic issues (Packard & Babineau, 2009; 1997). Some of the

more widely acknowledged contextual influences such as age, gender, and the cost of training will

now be discussed.

2.7.1 Age. That job and even occupational mobility are widespread in the first decade of

working life is well established (Ginzberg, 1951; Huang & Sverke, 2007; Kambourov &

Manovskii, 2008; Markey & Parks II, 1989; Rindfuss, Cooksey, & Sutterlin, 1999; Rothstein, 1980;

Sommers & Eck, 1977; 2010; Super, 1957; Topel & Ward, 1992). Neal (1999) found that workers

46

made fewer and less complex job changes as they aged. Similarly, age, and time in an occupation

or with a particular employer often determines an individual’s propensity and opportunity to

instigate a change (Kambourov & Manovskii, 2008; Rosenfeld, 1992). The lowered job mobility

rate with increased age (Parrado, 2007; Lale, 2012) has also been attributed to the achievement of a

job level ceiling commensurate with accumulated human capital, including educational

qualifications (Hachen, 1990; Sicherman & Galor, 1990). The age at which such a ceiling is

reached varies widely between different occupational groups and modes of practice (Thompson,

1968).

Acknowledged issues in changing to another profession as an older and thus non-traditional

new graduate entrant include gaining the respect of younger co-workers (Lewis, 1996), feeling that

skills and previous experience are not valued (Anthony & Ord, 2008), and the necessity of

revisiting the ‘novice’ role with associated feelings of insecurity, incompetence and loss of status

(Crow, Levine, & Nager, 1990; Haggard, Slostad, & Winterton, 2006). Balancing personal and

financial issues is also critical (Haggard et al 2006; Pitcher & Purcell, 1998).

Age and academic ability. Studies of possible age-related decline in cognitive abilities that

could limit options of field changes with aging have suggested a slowing in some aspects,

particularly in response speed and memory, but have also highlighted compensatory behaviours

such as attention to detail and association strategies for memory recall (Greller & Simpson, 1999;

Shimamura et al., 1995). The approach to study of mature-age students, especially with previous

university qualifications (Dickson, Fleet, & Watt, 2000), and their perseverance, are superior to

those of younger students (Richardson, 1995), and they can outperform younger students in

humanities courses but not in science (Smythe, Knuiman, Thornett, & Kiiveri, 1990). Mature-age

university students can also do well in mathematics courses but in a British study, success was

related to having the necessary prerequisite level of maths (A-levels) and being under age 35 (Hirst,

1999). There has also been concern about ageism reducing a fair assessment for older people who

changed occupation (Greller & Richtermeyer, 2006; Greller & Simpson, 1999; Pitcher & Purcell,

1998). For example, using graduate survey data, Sarason (1977) noted prejudice against mature-age

new graduates in some fields, particularly law, as an obstacle to occupational change. However, in

a study of Australian bachelor degree graduates, mature age was found to be an asset rather than a

liability in obtaining degree-related employment on graduation, though personal attributes and field

of study were also relevant (Chesters & Watson, 2013).

Life-stage considerations. The opportunity cost of changing between knowledge

occupations increases with age, so individuals contemplating an occupational transition or

disjunctive change need to consider whether, in view of the cost of training, and the expected salary

47

they should receive in their new occupation, they will be able to recover costs before retirement

(Egerton & Parry, 2001; Boeren et al, 2010; Bender & Heywood, 2011). Therefore the interaction

of their age and the actual field they are changing into are factors to be considered in a change

decision.

Undertaking a disjunctive change, or even some occupational transitions between

knowledge occupations may often compete with typical life stage processes such as establishing

financial independence, marriage, and parenthood (Newman, 2010; Pitcher & Purcell, 1998). Thus,

for balancing all life demands and responsibilities, a supportive family and social network is

paramount in both the decision to and the process of change (Neapolitan, 1980; Rathbun-Grubb,

2009; Robbins, 1978). The next section looks at gender in relation to occupational mobility and

change.

2.7.2 Gender. The effect of gender (Asiyabi & Mirabi, 2012; Betz, 2003; Birch, 2011;

Hotchkiss & Borow, 1996; Kilpatrick & Felmingham, 1996; Lent, Brown, & Hackett, 1996;

Sommers & Eck, 1977; Super, Super, & Savickas, 1996) and the gender-typing of occupations

(Beavis, 2007; Frank & Walters, 2012; Gottfredson, 2002; Williams, Muller, & Kilanski, 2012) on

occupational choices, mobility, and work patterns is well established (Kattenbach et al., 2011).

From Australian Bureau of Statistics census figures (2011), the fields of non-school study chosen

by individuals differed between males and females. Males were more likely to choose engineering

(21% of males, 2% of females) and also architecture and building (84% were males), whereas in

society and culture, and education programmes 69% and 79% respectively were females (Study for

a Qualification).

Social norms and role expectations. There appears to be a consensus in the literature that

men and women tend to enact their careers in accordance with societal norms and role expectations,

which brings about less linearity and more radical changes in women’s careers (Sterret, 1999;

Sullivan & Arthur, 2006). Women tend to take on lower level jobs, and their engagement with the

world of work tends to stop and start related to family responsibilities and geographic movements

following a spouse’s job change (Sterret, 1999; Sullivan & Arthur, 2006), revealing the “propensity

of women to make sacrifices in their careers for the sake of their husbands” (Valcour & Tolbert,

2003, p. 771).

Social role differentiation also impacts fields of occupational choice as women tend to

choose more flexible, family-friendly occupations such as teaching (Cooper & Davey, 2011), while

men look more to fields which promise options for lifelong career development, with perhaps

promotion into managerial positions. Generally, marriage and parenthood have a negative effect on

the attainment of occupational goals for both genders but the effect is much more pronounced for

48

females (Reynolds et al., 2007). The potentially intermittent nature of women’s availability for

employment indicates an incompatibility with the traditional model of career. Their higher mobility

between rather than within organisations, which differs from the mobility patterns of men’s careers

(Valcour & Tolbert, 2003), suggests that the boundaryless model may more appropriately describe

the career structures of females. However because of time out of both the workforce and further

education, for family care, it would be difficult for women to maintain or improve their expertise to

remain competitive in moving between organisations in knowledge occupations. In a broader

sense, the boundary between work and home, which may have related to and perhaps legitimised

women’s career patterns, has not been raised in the boundaryless model (Parker & Roan, 2015;

Pringle & Mallon, 2003). Further, with geoscientists in the oil and gas industry, Williams et al.

(2012) found that the mechanisms of institutionalising gender inequality in the traditional model

have been replaced by other processes to the same effect in the ‘new’ economy.

Gender bias. Relatedly, another factor found to influence occupational field choice among

females is gender bias. The introduction of equal employment legislation in many countries has

unfortunately not remedied the disadvantages women face in the labour market and the world of

work (Bimrose & Mulvey, 2015; McMahon, 2015). For example, within large organisations, male

MBA graduates attain higher levels of employment and of remuneration than females with

equivalent MBA qualifications (Mallon & Cohen, 2001; Reynolds, Burge, Robbins, Boyd, &

Harris, 2007; Still & Souter, 2005). Further in the USA high-achieving women in STEM majors

tend to receive lower returns than equivalent males (Olitsky, 2014). Gender discrimination has

been reported in other knowledge occupations such as engineering (Giles, Ski, & Vrdoljak, 2009).

Thus there are many aspects of gender that effect career directional and change decisions, and this

study will consider the effect of gender in relation to patterns of changing field of study.

2.7.3 Other contextual factors. The opportunity cost of changing has been the most

prominent issue highlighted by respondents in the few studies of attempted career change between

knowledge occupations (Farrell, 2009; Mullins, 2009; Neapolitan, 1980; Packard & Babineau,

2009; Robbins, 1978). The cost of the education is one consideration (Walters, 2003) which in

Australia can vary according to the length and level of the course (undergraduate or postgraduate),

the field of study, whether there are any Government subsidised places in the course, and if so, if

the potential student is eligible for one of those places. Students can choose to pay upfront or in

some cases student loans are available from the Government under the Higher Education

Contribution Scheme (HECS), which can be paid off over a long period of time depending on the

earnings of the student after graduation. Course length and mode of offering would influence the

decision to change depending on the ability of the student to maintain an income while studying.

49

Also the ease of transition into second or third occupations would vary according to the similarity of

occupational fields involved (Scott, 1997; 2007).

Thus, the availability of suitable programmes and modes of study at universities could

partially determine the level of up, add or change-skilling that individuals who already have a

university qualification undertake. This is in keeping with the delayed specialisation model

recommended by Malamud (2011), discussed earlier. Also, the availability of distance education

programmes could arguably increase participation in further study. Thus factors influencing

occupational mobility, occupational change decisions and outcomes in knowledge occupations are

wide-ranging, related not only to particular occupational issues, but also to age, gender, costs,

educational programmes on offer, and study mode options. The next section continues the

examination of influential factors by exploring motivations for changing occupations.

2.8. Motivations for Changing Occupation

While occupational mobility has been much discussed conceptually in the literature,

motivation for actual occupational change has not received much empirical attention, except in

relation to second-career teachers, possibly because disjunctive change is considered to be

uncommon (Blau, 2000; Markey, 1989). Improvements in wage and non-wage related conditions

of employment are seen by some as the prime motivators of occupational change (Baird, 2012;

Markey, 1989; Rathbun-Grubb, 2009). However, using the HILDA data, Carless and Arnup (2011)

found job security to be the prime reason for change in employed people who had made a change.

Reasons for change found by others include status, satisfaction, skill improvement, improved

opportunities for career development (Markey, 1989), overwork, poor management, flat career

trajectories, inflexible working conditions, geographical issues, conflicting work and family

responsibilities (Rathbun-Grubb, 2009), disillusionment, and wanting to make a contribution to

society (Evans, 2011). With helping professions involving intensive people contact, occupational

change resulted from emotional burnout, huge caseloads, lack of support, and general

dissatisfaction (Rathbun-Grubb, 2009).

Because actual skills-set change between knowledge occupations is uncommon (Blau, 2000;

Markey, 1989) only a few studies have focussed on it (Neapolitan, 1980; Robbins, 1978), though

there is some literature on fields chosen as second study. Business and education are noted as the

most common choices for second study, as discussed in the next section.

2.8.1 Adding a business qualification. Research has been undertaken on the popularity,

career usefulness and return for the investment of adding a qualification in business (Still & Souter,

2005), particularly in relation to graduate entry programs such as the Master of business

Administration (MBA) . Currently advanced degrees in business account for one quarter of all US

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masters degrees (Hann, 2014), but are losing popularity in China (Caplan, 2004), thought to

possibly reflect that the market is not valuing the MBA as highly as in the past. The MBA has been

described as a rite of passage to a developing career (Kelan & Jones, 2009), as providing tangible

and intangible inputs to career development, increasing human capital (Baruch, 2010), and as a

catalyst to increase career goal focus (Muja & Appelbaum, 2014a).

Though Chudzikowski (2012) described business alumni as “one of the most privileged

group of graduates characterised by a high level of employability” (p. 304), others have questioned

the relative advantage of an additional qualification in business, especially for females (Mallon &

Cohen, 2001; Reynolds et al., 2007; Still & Souter, 2005) or at mature age (Pitcher & Purcell,

1998). Thus business has been a popular and successful second study choice, but some questions

about been raised about the advantages of adding a qualification in business, particularly for

females. Teaching as a change option has actually been encouraged in the USA as discussed in the

next section.

2.8.2 Teaching as a change option. Though not part of a traditional or boundaryless

occupational trajectory (cf., business), a move from another occupation to become a second-career

teacher has become a recognised change option in more recent literature, in response to campaigns

to recruit mature-age graduates from other fields into teaching in many countries such as the USA

(Anderson, Fry, & Hourcade, 2014; Chambers, 2002; Coladonato, 2013; Crow et al., 1990; Evans,

2011; Lee, 2011; Schwartz, Wurtzel, & Olson, 2007), Australia, (Green, 2014; Kember, 2008;

Laming & Horne, 2013; Williams, 2005; Williams & Forgasz, 2009), New Zealand (Anthony &

Ord, 2008) and the United Kingdom (Griffiths, 2011). Coladonato (2013) estimated that one third

of new teachers in the USA have changed from another professional area of expertise into teaching

and “Teaching ‘is the second career of choice in the UK economy’ (BBC News, 2004)”(Newman,

2010, p. 473) A perceived attraction of education, as noted by Roksa (2010), is that the outcome is a

recognised and registered profession, which offers the graduate a professional identity, a range of

identifiable skills and knowledge, and a tight connection with the labour market, enabling easier

initial entry.

In summary, both positive motivations (to improve employment position and employment

security), and negative factors (to leave unsatisfactory aspects of employment), have been cited as

reasons for job or occupational change (Evans, 2011; Baird, 2012, Markey,1989; Rathbun-Grubb,

2009; Carless, 2011). Entering the field of education as a second-career teacher, or adding a

qualification in business, for example an MBA, have been the most popular second broadfield

choices.

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2.9. Summary of Literature Review

The ‘new’ careers/ knowledge economy future, with ongoing occupational mobility and

change, and high demand for knowledge workers, generally anticipated as a response to rapid

technological (digital) change and globalisation, appears to contain some inconsistencies.

Specifically, ‘knowledge workers’ (i.e., individuals in knowledge occupations) are generally

thought to have the lowest levels of occupational mobility. Thus, claims that the ‘new

careers’/knowledge economy offers increased mobility and increased specialisation appear

contradictory. Further, workforce mobility is multifaceted, which raises the question of the nature

of the future mobility envisaged with respect to knowledge occupations, particularly in relation to

actual skills-and-knowledge-based occupational change. Future career structures have been

variously modelled, where individuals self-manage their careers lifelong in an increasingly post-

corporate era; but such models do not distinguish between different occupational groups and have

attracted criticism from several perspectives, mainly because of lack of empirical validation, and

poor support from existing evidence.

The level of mobility in knowledge occupations also varies with age, life stage, the

opportunity cost of the change, gender, field of occupation, and suitable course offerings. Business

and education have been acknowledged as the most popular fields of extra credentialing for some

form of mobility in individuals in knowledge occupations. Overall, therefore, it is difficult to see

how the theories and models of career development and the ‘new careers’ narrative relates to high-

level ‘bounded’ occupations where change costs are high and mobility levels correspondingly low.

Thus the relevance of the initial choice of degree field and occupation in determining future career

development options is implied in the mobility literature, and has important implications for the

decision-making of university aspirants, for career guidance and for the efficacy of lifelong

learning.

2.10 Research Aims and Questions

As demonstrated in Chapter 1 and in the review of literature in this Chapter, occupational

mobility is frequently discussed in the context of self-constructed career paths or more generally as

an integral part of career development, but there has been very little empirical analysis of the

fundamental elements and processes modelled. Without a shared understanding of mobility, it is

very difficult to find a common, multidisciplinary, conceptual basis from which to compare and

evaluate the various theories and models of career process and development, or generally progress

the discipline towards a body of usable knowledge. As levels of mobility are thought to vary

between different occupational groups (Kambourov & Manovskii, 2008; Sicherman & Galor, 1990;

Super, 1980b; Sweet, 2011), to enable a focused examination of mobility, this study selected one

52

such group, knowledge occupations. Individuals in knowledge occupations were chosen for the

study of mobility because of their high level of initial investment and reportedly low level of future

occupational mobility (Elliott & Lindley, 2006; Feldman & Ng, 2007; Neal, 1999; Super, 1980b).

The research aims to:

1) Examine the fields of study of the initial and recent qualifications as indicators of the

possible future occupational intent of individuals who undertook further credentialing, and

thus gauge the level and nature of perceived mobility options.

2) Explore motivations for undertaking occupational transition and disjunctive occupational

change, the factors involved in undertaking such changes, and the expected outcomes.

To address these aims, the specific research questions are:

1. What percentage of students who already had at least a Bachelors Degree and who

undertook further tertiary study chose to study in a different field of study from that of a

previous qualification?

2. What were the patterns of change with respect to particular fields of study?

3. Were staying or changing patterns related to particular demographic or contextual factors,

and if so how?

4. Why did individuals with at least a Bachelors Degree, choose to study in a field different

from that of previous study?

5. How did demographic and contextual factors influence the decision to change field, or

affect outcomes?

53

Chapter 3

Method

In this chapter, the approach taken to address the aims of this thesis and the research

questions developed in Chapter 2 is discussed, together with the rationale for the mixed method

design of the study. The data source, sampling techniques, and data analytic methods used in both

the quantitative and qualitative components of the research are described, and the sources of error

are discussed.

The research in this thesis investigates actual patterns of occupational mobility with respect

to specific fields of knowledge occupations, and to contextual factors. For the purposes of this

study, knowledge occupations will be deemed to be those requiring a mandatory qualification at

least at Bachelor Degree level (Salt, 2015) level that is Australian Qualifications Framework (AQF)

level 7 (Department of Employment Education and Workplace Relations, 2011).

As obtaining at least an AQF level 7 qualifications has been defined as mandatory for

knowledge occupations, field of study will be generally considered an indicator of field of

occupation. Therefore a measure of the skills-based nature of occupational mobility in and between

knowledge occupations will be obtained by comparing the fields of study of individuals who have

at least two qualifications at AQF level 7, excluding the situation where both qualifications were

studied concurrently as part of a double degree. Individuals choosing to study in the same field as a

previous qualification will be considered to be up-skilling and those studying in a different field as

change-skilling, or possibly re-skilling.

3.2 Study Design

Little previous research has been conducted on the skill-based nature of patterns of

occupational mobility per se or disjunctive occupational change in knowledge occupations, in

relation to a wide range of specific fields of occupation. Therefore this research was investigative,

designed to respond to the research questions without a priori expectations of findings, and thus to

provide information for further study.

Research questions one, two, and three required a quantitative response, and questions four

and five necessitate a qualitative elaboration on the quantitative results. Therefore, an approach that

used an explanatory two-phase sequential form of mixed method design (MMD) was deemed

appropriate (Ivankova, Creswell, & Stick, 2006; Lieberman, 2005; McGraw, Zvonkovic, & Walker,

2000; Plano Clark, Huddleston-Casas, Churchill, O'Neil Green, & Garrett, 2008). This mixed

method design is also called a developmental design (Collins & O’Cathain, 2009) in which the

results from a quantitative analysis of a large data set (Phase One) are used to guide the selection of

54

participants for a much smaller, in depth qualitative study (Plano Clark, Creswell, O’Neil Green, &

Shope, 2008) (Phase Two). Qualitative inquiry provides a subjective perspective on the

quantitative results obtained and adds rigour, depth (Denzin & Lincoln, 1998) and credence to the

study through improved understanding of factors surrounding occupational change actions in

knowledge occupations.

Mixed method research is often recognised as the third major research paradigm (Johnson,

Onwuegbuzie, & Turner, 2007) and is particularly appropriate when neither a quantitative nor

qualitative approach would be sufficient to independently address the nature of the inquiry

(Ivankova et al., 2006). Using a MMD methodology, “the researcher collects and analyzes data,

integrates the findings, and draws inferences using both qualitative and quantitative approaches or

methods in a single study or a program of inquiry” (Tashakkori & Creswell, 2007, p. 4). Of

particular relevance is the requirement for the integration of findings, which distinguishes the added

value of a true MMD from the use of multiple yet independent data streams and analytic approaches

(Bryman, 2007; Castro, Kellison, Boyd, & Kopak, 2010; Johnson et al., 2007; Morgan, 1998).

In a sequential MMD, the sampling procedures for the quantitative and qualitative studies

are usually different with probability sampling utilised to obtain the quantitative data set and

purposive selection procedures for the qualitative study (Collins & O’Cathain, 2009). The

quantitative phase of this study (Phase One) interrogates a large existing secondary data set

(described later in this chapter), and therefore a parallel sampling design (Collins & O’Cathain,

2009) is appropriate where different individuals participate in the two sequences but will be drawn

from the same conceptual group, that is, Australians who already have an AQF level 7 qualification

and who opted to undertake further study at level seven or above.

The focus was on the quantitative study which had a very large sample size, and the

qualitative study was exploratory in nature and very small. Integration of phases occurred of

necessity in the intermediate period between the quantitative and qualitative sequences. Basing

some of the interview protocols on quantitative results could achieve further integration. In the

final discussion, findings from both phases were integrated (Plano Clark, Creswell, et al., 2008) in

relation to research questions, four and five. Each phase of the study is described below, beginning

with Phase One, the quantitative study.

3.2.1 Phase One: The quantitative study. This quantitative section begins with a

description of the Australian Graduate Survey data set (Graduate Careers Australia, 2008), and its

relevance for this research. The quality of the data available to address the research questions, is

then discussed followed by the process for arriving at the final analytic sample, sources of error in

55

the data, and analytical procedures used to respond to research questions 1, 2, and 3 that require a

quantitative response.

It was necessary to obtain a national dataset containing details of fields of study of earlier

and recent qualifications completed by individuals who had returned to university for further

credentialing. Fields of qualifications of each person could then be compared as indicators of levels

and patterns of mobility. Details of age, gender, study- mode and fee payment were also required to

explore possible influences on study field choices.

As it was not possible within the resources of this project to design and implement a

comprehensive survey for the collection of national data, an existing dataset providing information

on the research topic was identified and utilised for the purposes of secondary data analysis.

Though secondary data analysis has been defined in numerous ways (Smith, 2008), the concept

used here involved analysing existing data that was originally collected for another purpose by an

organisation or other researcher (Andersen, Prause, & Silver, 2011; Mulhern, 2010; Singleton,

1988; Smith, 2008). Data on field of study was critical to the research aims for this study, and the

Australian Graduate Survey Questionnaire was unique in containing questions that would elicit the

field of study of the required two qualifications at AQF level 7 or above/ per respondent.

The Australian Graduate Survey Data. The Australian Graduate Survey (AGS), conducted

annually by Graduate Careers Australia (Graduate Careers Australia, 2008), is designed to obtain

information from graduates in relation to the study programme they have just completed. In

addition to course experience evaluations, data is elicited on student demographics, the highest

previous qualification, mode of study, programme-funding mechanisms and on actions on

completion, such as embarking on further study, and engaging with the employment market. The

questionnaires are sent electronically or in paper form in April and October each year to all

graduates of all universities in Australia. In 2008, questionnaires were completed on line or on

paper and the response rate for the Australian residents surveyed was 61.1% (Graduate Careers

Australia, 2008). The data was collected by each university and was either entered and coded

directly or sent to Graduate Careers Australia as raw data to be coded. The 2008 AGS provided the

most recent data available at the time this study commenced.

The advantages of this data set are twofold. Firstly, the survey accessed all individuals in

Australia who completed qualifications at AQF level 7 or above at a specific point in time, that is in

2007. By eliminating respondents whose highest previous qualification was lower than AQF level

7, it was possible to collect all the people with a previous level 7 qualification completing an

additional qualification in 2007. Therefore, the data sample represents a snapshot in time of people

who already had at least a bachelors degree and were undertaking further qualifications. The

56

assumption here is that this sample was representative of further credentialing undertaken by

individuals in recent times. This assumption would seem reasonable but global economic events,

the political climate and perceived labour market demands could arguably affect the numbers and

fields of graduates in any given period. Though the survey is undertaken yearly, the 2008 data was

the most recent when this research commenced. Because of the complexity of data cleansing and

preparation with such a large database, and the necessity to obtain ethical clearance, it was not

possible to change databases within the time limitations of a PhD.

Secondly, the fit with the research questions was arguably good (Hofferth, 2005) because

the range of the various demographic variables available in the data such as age, gender, course

funding, and the type and modes of university offerings reflect the actual situation for adults

engaging in further tertiary study. In addition, the data recorded was objective rather than

interpretive reducing the chance of secondary data error (Smith, 2008).

Processing the data. Data analysis was undertaken using STATA statistical software,

Version 11 (StataCorp., 2009). It was necessary to format the data according to the requirements of

the statistical analyses undertaken using STATA. The data set contained responses from 119,447

individuals from 49 universities and smaller tertiary colleges throughout Australia. The full data set

was provided in an Excel spreadsheet and contained 255 variables corresponding to all questions

asked in the survey. Many variables (148) that were initially obviously irrelevant to the research,

for example, responses to course satisfaction issues, were eliminated from the dataset before

importation to the statistical software. Prior to the elimination, all original variables were assigned

a name to align with STATA specifications and to reflect the wording and code of the

corresponding question, for the purpose of easy identification. To avoid possible sources of error in

processing, each respondent was assigned a unique identification number.

Selection of sample. From 119,447 respondents, the analytic sample was derived using the

responses to three particular questions. Individuals were omitted from the sample if:

1. They were not Australian residents or indicated that they were paying International Student

Fees.

2. There was no response to the question on the name of highest previous qualification. The

name of the previous qualification was required as the only basis on which fields of study

could be compared.

3. The level of their highest previous qualification was less than AQF level 7.

4. There was insufficient detail about the field of study of their highest previous qualification.

The removal of respondents from the sample was based on the following rules:

Rule 1: They were not Australian residents.

57

Relevant Question: “Are you a citizen or permanent resident of Australia?”

Greater than 22,000 Respondents answered ‘No’ to this question and were eliminated,

reducing the sample to approximately 97,000 respondents.

Rule 2: There was no response to the question on the name of highest previous qualification.

Relevant Questions: “What was the full title of your highest previous educational

qualification? For example: “Bachelor of Commerce, Diploma of Education, Secondary Education

Certificate”

Greater than 63,000 records that did not contain a response were eliminated and 33,381

respondents remained in the sample.

It is important to note that in the GCA questionnaire, the question eliciting the name of

previous qualifications was only included as a check on the accuracy of response to a later question

on the level of the highest previous qualification, and was not intended to be used in any of the

GCA analyses. Some response data to this question is missing for two reasons. Firstly, institutions

that chose to code their own data were not required to necessarily include responses to this question

in data returned to GCA. Secondly, for those who passed their non-coded data to GCA, the

responses from graduates who entered the questionnaire responses online were present, but data-

entering staff at GCA were not required to enter data from hand-completed questionnaires since

such information would not be used. Similar issues affected the quality of data for other variables

intermittently, and at times the information had not been supplied by the respondent.

Responses were clustered by university, and missing data on the name of previous

qualification variable generally corresponded to particular universities. There did not appear to be

any pattern in relation to which institutions had chosen to code or not to code. To assess for any

bias in the sample that could result from the mix of universities whose response data was available,

the relative contribution from each group of institutions was considered. Universities in Australia

have chosen to either form alliances with other universities or to remain unaligned. There are three

main groupings of universities and another that was disbanded in 2007 (See Table 3.1). The

alliances are considered functional for, as an example, marketing and lobbying, and the institutions

in each group tend to share a style and focus ("Australian University Groupings,").

58

Table 3.1

Australian University Groupings

Name of Group of

Universities

Percentage of

National Total of

Students

Number with usable response

data on previous qualification

Group of 8 27

6 out of 8

Australian Technology

Network of Universities

(ATN)

20

3 out of 5

Innovative Research

Universities (IRUA) 15 3 out of 7

Australian New Generation

Universities (NGU)

Group disbanded in

2007 6 out of 10

Non-aligned Universities 38

4 out of 10

Without an extensive study of potential differences in the student characteristics or offerings

of these various institution groupings, it is difficult to determine bias from this source that may

contaminate findings from this study. As can be seen from Table 3.1, though the Group of 8 had a

higher percentage of member universities represented in the data set, all other groups were similar

to each other in levels of representation, so all responses that were within the rules were used.

After eliminating individuals without a response to the question on the name of highest

previous qualification in keeping with Rule 2: 33,381 respondents remained and all university

groups were reasonably represented.

Rule 3: The level of their highest previous qualification was less than AQF level 7.

Relevant Question: What is the level of this highest previous qualification?

Respondents were eliminated if their response to this question was “0”, ‘no previous

qualification’, “completed secondary education”, and “year 12”: 13,752 respondents were

eliminated and 19,629 remained.

Because inaccuracies were detected in the indication of the level of their highest previous

qualification, qualifications were grouped according to name, and decisions on inclusion/exclusion

were made according to the name of the qualification. With some exceptions, if a qualification had

59

the title of an undergraduate or Vocational education qualification, it was eliminated. For example,

qualifications eliminated included Associate Degree, Advanced Diploma, Diploma, Certificate I to

IV or advanced Certificate.

The exception to this decision was the Diploma of Education or Diploma of Teaching and

the Diploma of Psychology which are postgraduate qualifications throughout Australia that

originally predated the development of the AQF. Also, centres of vocational education do not offer

courses in either of these two professional areas. These three qualifications are currently named as

Graduate Diplomas by most universities, but in the past the word ‘Graduate’ was not included in the

title at many universities in Australia. Some universities still include these diplomas without the

word ‘Graduate’ particularly in dual programmes.

Rule 4: There was insufficient detail about the field of study of their highest

previous qualification: 8,327 Respondents with insufficient detail about the field of their highest

previous qualification were eliminated, and the number remaining after this elimination was 11,302.

The question on past study did not specifically request a major field of study, so coding

necessarily relied on the title of the qualification. In some cases where the lack of nomination of a

major could result in ambiguity, the respondent was eliminated from further analysis. Occasionally

it was not clear what the qualification was and those respondents were eliminated, e.g., “M

PH&TM".

If a respondent had listed their highest previous qualification without indicating a major

field of study, thus making it impossible to code the field of the response with any degree of

certainty, the record was eliminated. Titles such as Bachelor of Applied science with no field

specified were also eliminated because of the range of fields that various institutions include under

that title. It was decided to retain the Bachelor of Arts (BA) as it could generically be coded in the

field of society and culture. Though some universities offer a mathematics major in their BA, most

do not extend the majors beyond the broadfield of society and culture.

The following responses led to exclusion (See Table 3.2).

60

Table 3.2

Responses that led to exclusion

Excluded Reason

PhD If field of study not specified

Masters’ Degree If field of study not specified

Bachelors’ Degree If field of study not specified

Bachelor of Applied science *

If field of study not specified. oo wide a range of

possible study fields as the Institution of

accreditation was not known.

Double Degrees Because of ambiguity about intended major if the

major was not specified

*The Bachelor of Applied science can include a wide range of majors, for example:

Architectural science (Open Universities Australia); Outdoor Recreation and Ecotourism, Parks,

Recreation and Heritage (Charles Sturt University); Psychology (Deakin University);

Physiotherapy, Speech Pathology (University of Sydney); Agriculture and business, Nautical

science, Marine Engineering (University of Tasmania); Human Biology, Forensic science

(University of Canberra); Human Movement (Victoria University), Sports and Exercise science

(University of Western Sydney); Property and Valuation (RMIT).

A number of qualifications entered as highest previous qualification were actually

memberships of various professional associations, for example, ‘Certified Practising Accountant’,

rather than degrees. Many of these were deemed to be tertiary level qualifications because the

qualification required for such membership was at least a Bachelors degree (See Appendix 2).

The 11,302 remaining respondents were Australian residents who have a previous tertiary

qualification at least at AQF level 7 in an identifiable field and who had completed another

qualification at least at level 7 or above in an identifiable field in 2007.

Sources of error. As with difficulties discussed in the use of secondary data (Glaser, 1963;

Hofferth, 2005; Singleton, 1988; Smith, 2008), there will be some sources of potential error,

specifically:

1. Respondents were not required to list all previous qualifications or the dates of previous

qualifications.

2. Respondents were asked to nominate the major field of study for their recent study, but not

for their highest previous qualification, so the name of the qualification will be the only

indicator of the broadfield of past study. In some, more generic qualifications such as

Bachelor of Arts or Bachelor of Applied Science it may not always be possible to nominate

the major field of study accurately even as a broadfield.

61

3. Some respondents erroneously listed the name of their recent qualification additionally as

the name of their previous highest qualification. Where possible, these responses were

eliminated, but where the name of the recent qualification is missing, it was difficult to

detect this error which could bias the results, inflating the ‘staying in the same field’

category.

4. Some professional qualifications, for example Bachelor of Medicine, Bachelor of Surgery

(MBBS) are offered only on a postgraduate basis at some universities requiring the initial

completion of a focussed undergraduate degree as a prerequisite. Typically students would

complete a Bachelor of Science then enter the MBBS program, but this would have been

pre-planned as a standard selection, and not an example of an occupational field change.

Because there is no date on the previous qualification, it was not possible to eliminate this

source of bias in the direction of inflating the occupational field change numbers.

5. As mentioned, errors may result from the over representation of some types of universities

in the study.

Data coding. To enable comparison of fields of study of previous and recent qualifications,

codes from the Australian Standard Classification of Education (ASCED), the Australian Bureau of

Statistics (ABS) were utilised (2001). For the most part, information supplied by the ABS for

identifying ASCED codes was employed to assign codes, but occasionally, the code had to be

manually selected from ASCED descriptions, if the name of the qualification was not in the coder.

The ASCED has a three-tiered structure and categorises areas of study into 12 broadfields

each of which is then subdivided into narrow-fields, which in turn are further divided into detailed-

fields. Each level is assigned a two- digit code so that ASCED codes contain six digits. The first

two digits indicate the broadfield, the next two the narrow-field, and the final two digits indicate the

detailed-field. Broadfields are distinguished by theoretical content and the general purpose of study

in each field. The 12 broadfields are listed here:

1. Natural and Physical Sciences

2. Information Technology

3. Engineering and Related Technologies

4. Architecture and Building

5. Agriculture, Environmental and Related Studies

6. Health

7. Education

8. Management and Commerce

9. Society and Culture

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10. Creative Arts

11. Food Hospitality and Personal Services

12. Mixed Field Programmes

Because this project is focusing on programmes at Bachelor Degree level (Australian

Qualification Framework level 7) or above, broadfields 11 and 12 were excluded. Broadfield

number 11 focuses on areas such as food preparation and delivery plus beauty therapy which are

taught in courses below level 7. Similarly broadfield number 12 delineates more general education

programs such as literacy and numeracy skills, which again are not taught at level 7 or above.

The fields of past and recent study of each respondent were mostly compared at the two

digit level of aggregation, that is, the level of broadfields. To compare these fields and thus identify

whether the field of recent study constituted a change from that of the past study, both the major

field of recent study and the field of past study were assigned ASCED two-digit codes. The two

broadfields of study of each respondent were then compared to identify whether or not they had

changed their broadfield of study, and the patterns of change.

Narrow-fields within the broadfield of society and culture. Unlike other broadfields, the

narrow-fields within society and culture are quite diverse and are united only because they are seen

to relate to the organisation of society and to the influence of individuals and groups (Australian

Bureau of Statistics, 2001), rather than because there is generic underpinning knowledge. Narrow-

fields such as law, psychology, second language acquisition, archaeology, or economics would be

unlikely to share significant specific skills and knowledge, and the processes of service delivery

differ. Therefore to gain a better understanding of wider ranging change patterns following the

initial analysis of broadfield change-patterns, qualifications in the broadfield of society and culture

were further analysed according to their four- digit narrow-fields, that is on their second two digits,

to allow comparison of change patterns within the broadfield, and between society and culture

narrow-fields and the other broadfields.

Analytical Procedures. (See Table 3.3) Data was processed using the statistical program

STATA, Version 11 (StataCorp., 2009). The statistical analyses used to address each of the

research questions are described below. Results matrices are located in Chapter 4.

63

Table 3.3

Table of Analyses

Question

Number Variables Samples Statistical Method Outcome

1.

Major field of study

in recent

qualification and

of Highest Previous

Qualification

All respondents

who met the

selection criteria

z test for differences

in proportions

The proportions of

individuals changing

Broadfield

2.


in recent

qualification and

of Highest Previous

Qualification

All respondents

who met the

selection criteria

Chi squared

Compared first 2

digits and prepared

matrix

12 x 12 change/no change

matrix. Patterns of change-

in Broadfields of study

2.

As above, taking

each broadfield

separately

All respondents

who met the

selection criteria

and changed

broadfield


in proportions

Comparing the changes in

and out of each Broadfield

with the overall change

cohort to find the

contribution of each to the

overall change, changing

out, and changing in

cohorts

2. As above

All respondents

who met the

selection criteria

and changed

broadfield

Difference in size

between the recent

and past study

cohorts as a

percentage of past

study cohort for each

broadfield

The change ratios for each

broadfield

2.

Change ratios

calculated as above As above

Numerical rank

ordering of change

ratios

Groupings of Broadfields

according to their change

ratios

2.

Change-in/change

out proportions of

each broadfield

As above

Calculation of

change-in and

change-out

percentages for each

broadfield

Detailed change-in change-

out patterns for individual

broadfields in each group

2.

Narrow-fields of

past and recent

study in society and

culture

Respondents with

study in society

and culture

Z test of significance

of difference in

proportions

Matrix for comparison of

proportion changing

between society and

culture narrow-fields and

over all change proportion

64

Question

Number Variables Samples Statistical Method Outcome

2.


in recent

qualification and

of Highest Previous

Qualification in all

broadfields and in

narrow-fields of

society and culture

All respondents

who met the

selection criteria


in proportions

changing

Matrix. Changes between

the narrow-fields of

Society & Culture and

other broadfields.

3.

Age, gender, mode

of study, financing

study, time taken to

complete

As above

Chi squared test of

association and

logistic regression

to assess significance

of change or no

change

Looking at the interaction

of all above results with

the variables listed in

column 2.

Research Question 1:

The variable ‘major field of education one’ was compared with the variable ‘highest

previous qualification’ using the first two digits of the ASCED codes (broadfields) for each

individual, to identify incidences of change of field/no change of field and a matrix of the numbers

in the past and recent cohorts of each broadfield was generated (see Table 4.1).

Analyses: A z-test was used to test for a difference in the proportion of individuals changing

broadfield from the proportion not changing broadfield.

Research Question 2:

To estimate the relative patterns of changes into and out of each broadfield, the proportional

contributions of the change-out and change-in numbers for each broadfield to both the total change-

out and total change-in cohorts were calculated.

Analyses: z-tests of differences in proportions were used to test:

the proportion of both changes-out and changes-in to individual broadfields

with the overall change-out and change-in cohorts respectively, to test any

differences in proportions changing out and in to individual broadfields.

the proportions changing between society and culture narrow-fields with the

overall change proportion

Chi Squared tests of association were used to test the independence of broadfields of past

and recent study.

65

Method for addressing Research Question 3: Because of the size of the sample (11,302) and

the wide range of ages involved, (19 to 75) the age variable was divided into five groups, each of

which spanned ten years, for ease of analysis.

Chi squared tests of association were used to test the independence of:

age groups of respondents and fields of both past and recent study

age groups and changing or not changing broadfield

age group and changing into and out of particular broadfields

gender and changing into particular broadfields

age groups and pay types

pay types and changing/not changing broadfield

full/part time study and change/no change

z tests of differences in proportions were used to compare:

the distribution of males and females in the sample

the distribution of full/part time study

Logistic regression analyses were used to analyse the change/no change patterns obtained in

response to Question 1, 2, and 3 above, for the variables of age, gender, pay type, mode of study.

Because of the apparently low levels of disjunctive change in the quantitative phase,

individuals who had undertaken such a change were sort as participants for the qualitative phase.

3.2.2 Phase two: The qualitative study. Following the MMD, the selection of participants

for the qualitative study to investigate contextual issues related to the analytical results from phase 1

commenced on completion of the quantitative data analysis. Qualitative research was developed

from a need to gain an in-depth appreciation of social phenomena (Denzin & Lincoln, 1998).

Techniques such as case study, life story, observation and interview allow researchers to explore

participants’ opinions and reactions free from a priori response boundaries. Interview is arguably

the most appropriate of these techniques for the present study as the focus is at a particular point in

time rather than longitudinal, and opinions were required from people who had undertaken extra

credentialing in a different broadfield from that of their previous degree. In particular, the

qualitative study addresses research questions 4 and 5.

Participant sampling. As questions four and five focus on motivations, issues and

expectations of individuals who studied in a field different from that of their earlier study,

participants in the qualitative study were required to have at least an AQF level 7 qualification and

be engaging in or have completed another tertiary qualification in a different field of study.

Individuals who had merely moved from completing one qualification to beginning another without

having worked or tried to find work for at least two years in a knowledge occupation commensurate

66

with their initial degree were not selected because they were not considered to be attempting a

change of occupation. Purposive sampling where participants are located and chosen according to

specific qualities (Teddlie & Yu, 2007) was employed such that individuals had to meet the

education and world of work criterion described above. Only those who had changed field of study

were chosen.

Difficulties in locating potential participants from hidden or hard-to-reach populations have

been well recognised in qualitative research (Atkinson & Flint, 2003; Beauchemin & González-

Ferrer, 2011; Biernacki & Waldorf, 1981; Castillo, 2009; Faugier & Sargeant, 1997; Heckathorn,

1997; Noy, 2008; Streeton, Cooke, & Campbell, 2004). In the process of simple snowball-sampling

an initial subject refers another and the process continues in this way (Castillo, 2009). In the

present research, the underpinning assumption that members of the target population are linked in

some way (Atkinson & Flint, 2003) is not appropriate when individuals who meet the selection

criteria are not part of an identifiable group, do not know each other, are not listed on any

centralised information source and so are without a sampling frame of reference (Goodman, 2011).

Hornby and Symon (1994) describe yet another type of snowball sampling where “the

purpose … is not to establish a random or representative sample but rather to identify those people

who have information about the process” (p.169). It is particularly useful in explorative or

descriptive studies (Atkinson & Flint, 2003) as in the current project. Initial participants are located

through distributing selection criteria to appropriate social organisations or to professional or

personal contacts of the researcher. In his study of career change, Neapolitan (1980) used a similar

approach, observing that: “There is no statistically representative way to locate mid-career

occupational changers.” (p. 213). He located subjects through personal contacts, newspaper

advertisements, letters to graduate schools, and by searching newspapers for articles on changers.

Therefore there appears to be a less than rigorous approach in the frequent use of snowballing, but

ample precedent for participants in information studies of hard-to-reach populations to be selected

by researchers using preset criteria from volunteers located in an ad hoc manner from various

appropriate sources including organisations, newspaper advertising, and personal associates.

Phase Two required participants who could provide information about their experiences in

returning to study in a different field. The sample recruitment procedure employed was similar to

that used by Biernacki and Waldorf (1981), Pearson and Bieschke (2001), and Neapolitan (1980) in

his study of mid-career changers. To locate a suitable pool of volunteer participants from which to

select a criterion sample, information was distributed to several schools at an Australian university

and to social and professional contacts of the researcher, and volunteers were asked to refer

acquaintances who they thought would meet the criteria. However locating individuals who met the

67

selection criteria, was particularly difficult and not perfectly achieved in the group selected in that

two of the participants had worked for only one year after their initial degree rather than the desired

two, before deciding to return to study.

Optimum sample sizes vary widely with the nature of the inquiry (Fusch & Ness, 2015), the

quality of the data, (Hagaman & Wutich, 2016) the research purpose, and the epistemological and

ontological position of the researcher (Saunders & Townsend, 2016) . In this open ended,

exploratory study analysis was to be data driven and thus inductive rather than deductive (O’Reilly

& Parker, 2013). The importance of data quality and transparency as emphasised by O’Reilly and

Parker has been heeded in this study which aimed to provide a nuanced annotation of the

quantitative findings, to seed further research into issues related to changing between knowledge

occupations.

The sample. Nine individuals were identified as meeting the criteria and agreed to

participate. Several others were suggested during the snowball sampling process but did not meet

the criteria mostly because one of their qualifications was not at tertiary level. The participants

were six females and three males and ranged in age from 27 to 60. Seven had completed their

second qualification and two were currently studying for theirs and thus planning their occupational

outcome. One of these was studying full-time at the time of interview. Seven had worked or

sought work in an occupation related to their initial degree for at least two years before returning to

study. The other two had commenced study in a new broadfield after one year of employment in

their initial degree–related occupation, but had continued to work in that occupation while studying.

Of these two, one had returned to study for interest reasons only, with no initial intention of

occupational change. Eight were working mostly full time at the time of the interview. Of those

who had completed their second qualification, four had graduated less than five years before, and

the remaining three had had at least ten years in the work force after their second graduation. These

variations within the group allowed for observation of issues faced by individuals undertaking the

process of broadfield change, and of some longitudinal outcomes of broadfield changes.

Procedure. Participants were provided with invitations to participate, information sheets,

and were required to sign consent forms (see Appendix 3). Procedural information was explained

and participants were reassured that their responses would be treated confidentially and that no

identifying information would be published. Interviewees were invited to peruse transcripts of their

interviews for accuracy. The objective of the interviews was to elicit contextual factors, issues and

motivations involved in fields of study change choices Given the explanatory nature of the study the

interviewer took an unbiased role, which provides the most effective way of generating a sufficient

quantity of valid information with minimal interviewer bias (Roulston, 2010). Further, semi-

68

structured interviews rather than a survey are more appropriate to generate in-depth, new

information (Roulston, 2010) as the interviewer is able to both control the direction of and topics

discussed in the interview and to explore responses in more depth by sensitive follow up

The interview process. The participants were volunteers who had provided their consent

after being sent a participant information sheet and consent form (see Appendix 3). The interviews

were conducted by the researcher at the homes of the participants or on three occasions at outside

venues at the preference of the particular participants. Interviews were semi–structured and

between 50 and 90 minutes in length with 24 questions. The questions were largely open-ended so

that issues mentioned could be and were explored with follow up inquiries (see Appendix 1), until

the researcher felt all relevant aspects of issues raised had been thoroughly covered. This flexibility

was necessary as each individual’s demographic and contextual circumstances and fields of

qualifications varied, which may have impacted their experiences and actions. The initial four

questions sought the demographic and contextual information, and a timeline of academic

qualifications. From there, questions explored a chronological, in-depth narrative of each

individual’s career development: five questions focussed on the decision-making process leading to

the initial degree selection and expected outcomes, two considered the initial degree experience,

one explored actions after initial graduation, one looked at reasons for the decision to change, five

explored second degree decision-making and the experiences of the process of change, two

considered the second university experience in comparison with the first, and four focussed on

outcomes from the second qualification. Factors in the decision-making processes throughout

included inputs from relevant others, the study experience for each qualification, employment

experiences after each qualification, factors leading to the decision to return to study in a different

field, expectations, any practical issues noted, reactions of significant others, the social and family

situation, and evaluation of the decision to return to study. Interviews were recorded and

transcribed by the researcher and sent to participants for checking. From their responses, no

problems with accuracy of the transcripts or the information given were detected.

Data Analysis. After investigations of NVivo and Leximancer as possible data analysis

tools, a rigorous manual analysis was employed to enable better appreciation of the subtleties of

language, and thus yield a more in-depth analysis. The thematic analysis followed a six phase

process described by Braun and Clarke (2006). In step one, after completing each interview, the

researcher transcribed the data for that interview and read each one several times to understand the

story of each participant. This process lead to familiarity with the data.

Step two involved the generation of initial codes and collation of all the material relevant to

each code. As the semi-structured interviews were focused on obtaining exploratory information

69

relating to the two research questions, a ‘data-driven’ approach (Braun & Clarke, 2006) to coding

was adopted so that there was no attempt to code according to a pre-existing framework, for

example, in response to the research questions, or to theoretical interests of the researcher. All

identified issues, thoughts, features, ideas etc., present in the data were given equal consideration

(Morse, 1995) and were coded. Using colour highlights in MS Word, the data was inclusively

coded, interview by interview, for as many potential ideas as possible. This process initially

generated 49 codes. However as some codes were only present on a few occasions, and others were

about related issues, there was some collapsing of codes into related codes before the data segments

relating to the more frequently appearing codes were collated, resulting in 28 codes. The more

frequently appearing codes included Reasons for course or broadfield selection’, ‘Research on

course and planning’, ‘Interests and motivations’, ‘Reasons for undertaking further study’, plus

several related to financial issues, and to the age issue.

Next, in keeping with step three, codes were further grouped into potential themes and the

data relevant to each was collated. To achieve this collation of data, each interview was re-read to

ensure all relevant material was collected. Some coded material contained elements relevant to

multiple themes and were included in the data collation of all the themes they applied to. During

this process, some themes began to emerge particularly age issues, including life-stages, immaturity

and limitations on outcomes, financial issues, difficulties with employment after first degree,

practical issues for second degree, decision-making process description, the motivation for

changing field of study and outcomes, so that there were nine themes after step three (see Table

3.4).

Step four was about reviewing the themes that had emerged in step three. To check the

themes, the transcripts were re-read. At this point the themes described seemed relevant but

interrelated and conceptual decisions had to be made about where particular data best belonged.

For example, ‘immaturity’ was acknowledged by many participants as partly related to their age but

was also identified as impacting on their decision-making. Also the concept of ‘life-stage tasks’

was not specifically named by any participant but from various comments was underlying many of

the reasons for particular decisions that were made. Because of the obvious relationship with age,

life-stage issues were included as a subtheme of age. There were six themes after step four (see

Table 3.4).

Step five was taken as an extension of step four and the two tended to blend into an ongoing

process of refining and revising the themes and sub themes to reach a kind of essence for each.

After step, five there were five main themes: decision-making processes; age, practical issues

(including financial, and course related issues), motivations for changing field of study, and

70

outcomes. For the final review, step six, the themes were again revisited this time with reference to

the research questions. The aspect from which to view an issue was also re-considered. For

example, dissatisfaction with various problems with employment outcomes was raised by many as a

reason for returning to study. Because after re-reading the reports of several participants it seemed

apparent that the reasons some, though not all participants, couldn’t find satisfaction in employment

were presented as practical rather than subjective. Also, the theme contained data on reasons for

selection of the new field and course. So the subjective term ‘motivations’ no longer accurately

described the data included in the theme and the title was altered to reflect the changes and became

‘Returning to Study’. Thus the intent of the Braun and Clarke (2006) process was followed but

after step three when themes were identified, the further steps probably merged into an ongoing

read and review process and it was difficult to identify where one step was completed and the next

started. Finally, the main themes, ‘Decision-Making Processes’ and ‘Individual-in-Situation’

address the research question four: Why did individuals who already had a degree return to study in

a different broadfield; and the main themes ‘Returning to Study’, and ‘Outcomes of the Change’

relate to research question five: How did demographic and contextual variables affect the decision

to return to study in a different broadfield (see Table 3.4).

71

Table 3.4

Development of the Thematic Structure

Step 2: Codes after initial

collation Step 3: Sub-Themes

Steps 4 and5:

Themes

Step 6:

Main

Themes

Research on course and

planning.

Individual approaches to

choosing a qualification

In Initial decision

In change decision

Individual

Approaches

Decision-

Making

Processes Consultation in choosing a field

of occupation.

In initial decision

In change decision

Consultation

Age

Immaturity

Life stage issues

Course demands on parents

Age related recruitment policies

Maturity issues

Life stage issues

Age and the work force

Age

Individual-

in-

Situation Course fees

Income

Financial support

Paying for the courses

Funding living while studying

change qualification

New graduate salaries

Financial

Course experience

Degree did not prepare people

for the real world

Dissatisfaction with initial

degree

Dissatisfaction with initial

degree process and content

Reasons for changing

broadfield

Returning

to Study

Difficulty finding degree related

work after initial degree

Problems at work

Dissatisfaction with

employment outcomes

Disillusionment with

profession

Disillusionment with profession

Reasons for particular new

field selection

Considering course

admission requirements,

course length, and study

mode

Choosing a change

broadfield Interests and Motivations

Reason for New Field and

Course Selection

Course offerings

Job achieved

Employment

following change

qualification

Outcomes

of the

change

Was change disjunctive Perceived skills and

knowledge –based

nature of change

Satisfaction with outcome Satisfaction with

outcome

72

Validity (Trustworthiness). As qualitative researchers “strive for understanding” (Crestwell,

1998, p. 193), the issues of researcher subjectivity (Frost et al., 2010) and of establishing validity

and reproducibility has been a source of debate for qualitative research (Freeman, deMarrais,

Preissle, Roulston, & St. Pierre, 2007; Guba, 1981). Seidman (2006) argues for the importance of

respect for validity and trustworthiness, but believes quantitative processes are not the only key to

effective research. . As Hammarberg, K. Kirkman, M. and de Lacey, S.(2016) contend: “We need

to be able to capture real-life experiences, which cannot be identical from one person to the next.”

(pp 499). Similarly, Frost et al. (2010) agree that qualitative research cannot seek absolutes, and

Freeman et al., that it should not be restricted to standard checklists (Freeman et al., 2007).

Thus while there is general acceptance of the importance of establishing rigour in qualitative

research, Morse et al. (2002) criticise some methods purported to achieve it (Morse, Barrett, Mayan,

Olson, & Spiers, 2002). Increasing sample sizes may sacrifice depth for breadth and does not

produce greater applicability (Hammarberg et al, 2016). Instead, utilising the flexible, open and

subtle processes which are the strengths of qualitative research (Freeman et al., 2007), quality can

be constructed and maintained in an ongoing way through the life of a project as researchers

“interact with those they study and as they consider their analyses, interpretations, and

representations of data” (Freeman et al., 2007, p. 27). The suggestions of Freeman et al. to achieve

quality in qualitative research were heeded in the present research. The processes of participant

selection, interviewing, transcribing, analysing and interpreting the data were conducted solely by

one researcher who is an experienced interviewer (Roulston, 2010) and was able to establish rapport

with participants, avoid interviewer bias and listen actively to develop an accurate appreciation of

the story each was presenting. The quantitative/qualitative structure of this mixed method

explanatory study provided a mechanism for the results of phase two to provide some insight into

phase 1, thus strengthening the validity of the research.

Ethical Issues. In accordance with the National Statement on Ethical Conduct in Human

Research (2007, p. 11), to “respect the privacy, confidentiality and cultural sensitivities of

participants”, project information sheets were given to potential participants, detailing the study

purpose and the interview process. They were invited to volunteer, the confidentiality protocols

were described, and they were reassured they would be free to withdraw at any time without

prejudice. Ethical clearance was obtained from the School of Education Ethical Review Committee

(see Appendix 4). The study does not seek to compare universities that participated in the AGS in

2008 in any way, in accordance with the conditions under which the data was released. Individual

institutions are not named in relation to survey results.

73

3.3 Chapter Summary

This research employed a mixed method design where phase one consisted of a quantitative

analysis of graduate student data from a large secondary database, the results of which influenced

the criteria for selection of interview participants for the qualitative phase two. Participants who

met the selection criteria were located by a type of snowball-sampling and the interview material

coded and arranged into four themes. Chapters 4, and 5, present the results of the quantitative

phase, and the qualitative phase results are provided in Chapters 6 and 7.

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Chapter 4

Results of Phase 1: Broadfields and Patterns of change

This is the first of four empirical chapters, each of which addresses select research

questions. In this chapter, findings are presented that address Phase 1 Research Questions 1 and 2

on the associations of broadfields of study with patterns of change. In Chapter 5, findings

addressing Phase 1 Question 3 on the influence of contextual variables are discussed. Chapters 6

and 7, describe the qualitative findings obtained from the Phase 2 interview responses.

In this chapter, the results from the first two parts of the Phase 1 data analyses are shown

and discussed, commencing with an overall measure of the proportion of individuals who changed

their broadfield of study such that their more recent study was in a different broadfield from that of

their past study. Secondly, with this cohort of individuals who had made a change, patterns of

broadfield change were obtained by comparing the proportions of changes into and out of each

broadfield. The contribution of movement into and out of a broadfield to the total change that

occurred, and the patterns of change between particular broadfields and some narrow-fields were

also examined. Results from the data analyses in response to Questions 1 and 2 are discussed in

order.

4.1 Patterns of Change in Broadfields of Study

Research Question 1: What percentage of students who already had at least a Bachelors

Degree and who undertook further tertiary study chose to study in a different field of study from

that of a previous qualification?

4.1.1 The proportions of individuals changing broadfield. The proportions of individuals

changing and not changing broadfield in their recent study were examined. Overall, of the 11,302

respondents from the 2008 Graduate Survey data who met the inclusion criteria, slightly more than

half (52.0%) continued studies in the same broadfield, while slightly fewer than half (48.0%) chose

to study in a different broadfield from that of their past study. Due to the large size of the sample,

this distribution of outcomes was significantly different from a position where the percentages

changing and not changing broadfield were approximately equal (z = -4.5, p<0.001).

For ease of comparison, a matrix was prepared for the broadfields of past and recent study

(see Table 4.1). In Table 4.1, the cell indicating no change for each broadfield has been highlighted

and together they form the diagonal of the matrix. The number of respondents who studied in the

same broadfield as that of their past study for all broadfields except Environmental Studies, was

greater than the numbers selecting any individual alternative broadfield. A Chi-squared test of

75

association performed on the matrix indicated that the fields of past and recent study were not

independent. (χ2 (110) = 21,000, P<0.001).

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Table 4.1

Overall Past and Recent Study Matrix showing the numbers of individuals with past and recent study in all Broadfields. The proportions in brackets

on the diagonal refer to the proportion of individuals who did not change broadfield.

Broadfield Past Study

Recent Study↓ 1 2 3 4 5 6 7 8 9 10 Total Recent

1 580

(0.34) 12 35 1 32 59 20 26 40 5 810

2 68

114

(0.34) 40 0 2 4 11 50 36 16 341

3 52

20

308

(0.42) 6 3 9 4 25 8 4 439

4 12

2 17

151

(0.72) 8 4 4 22 34 43 297

5 67

0 7 5

55

(0.29) 5 5 15 27 5 191

6 391

11 25 2 13

1,053

(0.67) 55 94 179 24 1,847

7 149 31 25 8 25 130 624

(0.57) 161 538 140 1,831

8 191 107 217 16 26 118 82 1,149

(0.61) 398 70 2,374

9 178 29 49 19 22 125 229 308 1,647

(0.54) 84 2,690

10 17 6 5 2 2 9 59 20 150 212

(0.35) 482

Total Past 1,705 332 728 210 188 1,516 1,093 1,870 3,057 603 11,302

Note. Key to the broadfields: 1. Science, 2. Information Technology, 3. Engineering, 4. Architecture and Building,

5. Agriculture and Environment, 6. Health, 7. Education, 8. business, 9. Society & Culture, 10. Creative Industries

` 77

4.1.2 Patterns of change with respect to particular broadfields.

Research Question 2. What were the patterns of change with respect to particular

Broadfields?

To address Question 2, the proportions of respondents changing into and out of each

broadfield were calculated. The numbers changing into each broadfield were then expressed as a

proportion of the total number of respondents that made a change of broadfield. Similarly the

numbers changing out of each broadfield were expressed as a proportion of the total number who

changed broadfield. In addition, change ratios were calculated as a within–broadfield indicator of

the degree of change/no change for each broadfield, taking account of the size of the broadfield and

thus enabling comparisons of the degrees of change into and out of each broadfield. The

broadfields were then grouped according to the change ratios and the comparisons of change

proportions described.

Concepts to aid analysis. This section describes several concepts used to assist with the

analysis of patterns of broadfield change. These concepts relate to past and recent study cohorts,

change cohorts, and, as referred to above, the change ratio.

Past and recent study cohorts. Respondents each belonged to two broadfield cohorts: the

cohort of the broadfield of their past study and the cohort of the broadfield of their recent study.

Similarly, each broadfield had two cohorts: a past study cohort, and a recent study cohort. In Table

4.1, the numbers in the last cell in each column, titled, ‘Total’, are the numbers of people in the past

study cohort for each broadfield and they total to 11,302. Similarly, the numbers in the last cell of

each row, also entitled ‘Total’ are the numbers in the recent study cohort for each broadfield. It is

interesting to compare the size of the past study cohort and recent study cohort in each broadfield.

For example, from Table 4.1, in the broadfield of science, the past study cohort was 1,705 and the

recent study cohort was 810. Using the size of the past study cohort and recent study cohort for

each broadfield, the proportion of each broadfield in the overall past study cohort and the overall

recent study cohort were compared.

For each broadfield, the recent study cohort consists of all those who remained in the same

broadfield plus those who changed into the broadfield. In Figure 4.1, the broadfield of science is

used as an example (see Figure 4.1).

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Figure 4.1. Changes out of and into science (an example of the change process)

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Figure 4.2. The Proportional Broadfield Composition of the Change-out Cohort

Figure 4.3. The Proportional Broadfield Composition of the Change-in Cohort.

The concept of the change cohorts. Interacting with the past and recent study cohorts is the

dichotomy of ‘change’ or ‘no change’. Of the respondents, 48% had made a change of broadfield.

These respondents had changed out of their broadfield of past study and so formed the change-out

` 80

cohort (See Figure 4.1). They had also changed into new broadfields, and were thus also the

change-in cohort (See Figure 4.2). Though these two cohorts obviously contained the same

respondents, that is, those who changed broadfield, it was useful to refer to them separately to

compare the proportional contribution that each broadfield had to overall change-out and change-in

cohorts (See Table 4.2 & 4.3 and Figures 4.1 & 4.2).

Past and recent study cohorts and change. A change-out percentage was calculated for each

broadfield to indicate the number changing out as a percentage of the past study cohort. The

numbers in the past study cohort of broadfields varied widely (see Table 4.1). Some broadfields

were much larger than others, so using a percentage of the past study cohort as a measure of

change-out, overcame the problem of size when the amount of change for various broadfields was

being compared. The equations used in the calculations are included here for ease of reference to

the tables where the results are listed. The formulae used to calculate the relevant percentages

were:

The percentage that each broadfield past study cohort was of the overall past study cohort

= 𝑃𝑎𝑠𝑡 𝑆𝑡𝑢𝑑𝑦 𝐶𝑜ℎ𝑜𝑟𝑡 (𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)

5409 × 100 (See Table 4.4).

The contribution of the number changing out of each broadfield to the change-out cohort

= 𝑃𝑆𝐶(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑) − 𝑁𝑢𝑚𝑏𝑒𝑟 𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔(𝑚𝑎𝑡𝑟𝑖𝑥𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙)

5409× 100 (See Table 4.4).

Percentage of broadfield changing out

= 𝑃𝑆𝐶(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑) − 𝑁𝑢𝑚𝑏𝑒𝑟 𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔(𝑚𝑎𝑡𝑟𝑖𝑥𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙)

𝑃𝑎𝑠𝑡 𝑆𝑡𝑢𝑑𝑦 𝐶𝑜ℎ𝑜𝑟𝑡 (𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)× 100 (See Table 4.2).

Similarly, calculations were made for the recent study cohort for each broadfield.

The percentage each broadfield recent study cohort was of the overall recent study cohort

=𝑅𝑒𝑐𝑒𝑛𝑡 𝑆𝑡𝑢𝑑𝑦 𝐶𝑜ℎ𝑜𝑟𝑡 (𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)

5409 × 100 (See Table 4.5).

The contribution of the number changing into each broadfield to the change-in cohort

=𝑅𝑆𝐶(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑) − 𝑁𝑢𝑚𝑏𝑒𝑟 𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔(𝑚𝑎𝑡𝑟𝑖𝑥 𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙)

5409× 100 (See Table 4.5).

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Percentage of broadfield changing in:

= 𝑅 𝑆 𝐶(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)−𝑁𝑢𝑚𝑏𝑒𝑟 𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔(𝐷𝑖𝑎𝑔𝑜𝑛𝑎𝑙 𝑇𝑎𝑏𝑙𝑒 1)

𝑅𝑒𝑐𝑒𝑛𝑡 𝐶𝑜ℎ𝑜𝑟𝑡 (𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)× 100 (See Table 4.3).

The change ratio. To examine the net gain or loss of students in individual broadfields

using the size of the past study cohort as a baseline, the difference between the number changing in

and the number changing out was calculated as a fraction of the past study cohort, expressed as a

percentage and titled the change ratio. If the change ratio was negative, the broadfield lost

participation relative to previous participation in the broadfield courses; and if positive, the

broadfield gained participation as people returned to study a different qualification. This figure gave

an indication of which broadfields attracted people changing out of their previous majors into new

fields and, conversely, which broadfields lost participants to other broadfields.

Calculating the change ratio:

= 𝑁𝑢𝑚𝑏𝑒𝑟 𝐶ℎ𝑎𝑛𝑔𝑖𝑛𝑔 𝑖𝑛𝑡𝑜 𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑 − 𝑁𝑢𝑚𝑏𝑒𝑟 𝐶ℎ𝑎𝑛𝑔𝑖𝑛𝑔 𝑜𝑢𝑡 𝑜𝑓 𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑

𝑃𝑎𝑠𝑡 𝑆𝑡𝑢𝑑𝑦 𝐶𝑜ℎ𝑜𝑟𝑡 (𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑)× 100

= (𝑅𝑆𝐶 𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑−𝑁𝑢𝑚𝑏𝑒𝑟 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔)–(𝑃𝑆𝐶𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑−𝑁𝑢𝑚𝑏𝑒𝑟 𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑖𝑛𝑔)

𝑃𝑎𝑠𝑡 𝑆𝑡𝑢𝑑𝑦 𝐶𝑜ℎ𝑜𝑟𝑡(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)×100

=𝑅𝑆𝐶 (𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑)−𝑃𝑆𝐶 (𝑏𝑟𝑜𝑎𝑑𝑓𝑖𝑒𝑙𝑑)

𝑃 𝑆 𝐶(𝑏𝑟𝑜𝑎𝑑 𝑓𝑖𝑒𝑙𝑑)× 100 (See Table 4.5).

For example: the change ratio for the broadfield of science = 810−1705

1705 × 100

= −52.5%.

Comparing the change in each broadfield with the overall change. To test whether the

percentage changing out of individual broadfields differed significantly from the overall change

percentage of 48%, statistical z scores were calculated. Similarly, the proportion of those changing

broadfield into each recent study cohort was compared with the overall change mean of 48% using

a z test (see Tables 4.2 and 4.3). The relatively low change-in percentages and the negative z score

associated with changing out of both science and engineering contrasted with the high proportions

and positive z scores associated with changing into education and business. Agriculture and

environmental studies had the highest change-in proportion, but overall numbers in that broadfield

were relatively low.

` 82

Table 4.2

Proportion changing out of broadfields compared with overall proportion changing (0.48)

Field No. Changing out Proportion

Changing out z score past study cohort

Science 1125 0.66 14.8** 1705

IT 218 0.66 6.4** 332

Engineering 420 0.58 5.3** 728

Architecture 59 0.28 -5.6** 210

Agriculture & Environment 133 0.71 6.2** 188

Health 463 0.31 -13.3** 1516

Education 469 0.43 -3.3** 1093

Business 721 0.39 -8.0** 1870

Society & Culture 1410 0.46 -1.9 3057

Creative Industries 391 0.65 8.2** 603

** p< 0.01

Table 4.3

Proportion changing into broadfields compared with overall proportion changing (0.48)

Field No. changing in Proportion

changing in z score

Recent

Study Cohort

Science 230 0.28 -11.1** 810

IT 227 0.67 6.9** 341

Engineering 131 0.30 -7.6** 439

Architecture 146 0.49 0.4 297

Agriculture & Environment 136 0.71 6.4** 191

Health 794 0.43 -4.6** 1847

Education 1207 0.66 15.4** 1831

Business 1225 0.52 3.7** 2374

Society & Culture 1043 0.39 -9.5** 2690

Creative Industries 270 0.56 3.5** 482

** p < 0.01

From Tables 4.2 and 4.3, with the exception of changes into architecture and changes out of

society and culture, all changes into and out of all individual broadfields were significantly different

from the overall change proportion of .48 (p< .01)

For comparison purposes, Table 4.4 provides a list of broadfields in order of the size of their

past study cohort. The percentage of overall past study cohort indicates the percentage that each

broadfield past study cohort was of the overall sample. Column 4 (Percentage Changing Out) lists

` 83

those changing out as a proportion of the past study cohort and Column 5 (Contribution to the

change-out Cohort) contains the numbers of respondents changing out of each broadfield as a

percentage of the total number changing (5,409).

Table 4.5 entries are similar to those in Table 4.4 except that the Table contains information

about the recent study cohort of each broadfield. Column 6 (change ratios) contains the change ratio

for each broadfield which provides a measure of the degree of change-in numbers for each

broadfield from the past study cohort to the recent study cohort. With the broadfields in both

Tables 4.4 and 4.5 listed in order of past and recent cohort size respectively, each broadfield has

been again allocated the colour assigned in the legend to Figure 4.2 to visually demonstrate the

comparative positions of each in terms of past and recent cohort size and the size of the change

ratios as graphed in Figure 4.3. Figure 4.3 displays the change ratios for all broadfields. The

proportional reduction in student numbers in science was greater than that in any other broadfield,

whereas education showed the largest gain.

From Tables 4.4 and 4.5, the STEM fields of science and engineering made up 21.5% of the

overall past study cohort (See Table 4.4), but combined changes out of these two broadfields made

up 28.8% of the change-out cohort, while changes in accounted for only 6.7% of the change-in

cohort (see Table 4.5). Changes into engineering (6.4% of overall past study cohort) accounted for

only 2.4% of the change-in cohort (see Table 4.5), and so less than any other broadfield.

From Table 4.5, 78.5% of all changes into broadfields were made up of changes into the

fields of business, education, society and culture, plus health which together only made up 66.7% of

the overall past study cohort. From Table 4.5, education and business comprised 26.2% of the

overall past study cohort but changes into those broadfields combined accounted for nearly 45% of

the change-in cohort. A combination of the remaining six broadfields made up only 21% of the

change-in cohort whereas their past study cohorts combined were one third of the overall past study

cohort. Therefore, the broadfields of science and engineering experienced a marked loss of

participation as students gained further qualifications in alternative fields, whereas the broadfields

of business and education experienced an increase in participation.

` 84

Table 4.4

Past Study Cohorts and % Changing Out

Broadfield

Past

study

cohort

Percentage of

overall past

study cohort

%

No.

Changing

out

Percentage

changing out

%

Contribution to

change-out

Cohort

%

Society &

Culture 3057 27.0 1410 46 26.0

Business 1870 16.5 721 39 13.3

Science 1705 15.0 1125 66 20.8

Health 1516 13.5 463 31 8.6

Education 1093 9.6 469 43 8.7

Engineering 728 6.5 420 58 7.8

Creative Ind 603 5.3 391 65 7.2

Info Tech 332 2.9 218 66 4.0

Arch/Bldg 210 1.9 59 28 1.1

Agr/Env 188 1.7 133 71 2.5

Table 4.5

Recent Study Cohorts and % Changing In

Broadfield

Recent

study

cohort

Percentage of

Overall

recent study

cohort

%

No.

Changing

in

Percentage

changing in

%

Contribution

to change

in Cohort

%

Change

ratio

%

Society &

Culture 2609 23.8 1043 39 19.2 -12.0

Business 2374 21.0 1228 56 22.6 +27.0

Health 1847 16.3 794 43 14.7 +21.8

Education 1831 16.2 1207 66 22.2 +60.8

Science 810 7.2 230 28 4.2 -52.5

Creat. Ind 482 4.2 270 56 5.0 -20.0

Info Tech 341 3.0 227 67 4.2 +2.6

Engineering 439 3.9 131 30 2.4 -40.0

Arch/Bldg 297 2.6 146 49 2.7 +41.4

Agr/Env 191 1.7 136 71 2.5 1.6

Figure 4.4 displays the change ratios for all broadfields and shows that the proportional

reduction in student numbers in science was greater than that in any other broadfield, whereas

education showed the largest gain.

` 85

Figure 4.4. Change ratios by Broadfield

4.2 Categorising Broadfields.

Tables 4.4 and 4.5 show that there was considerable movement between different

broadfields when individuals returned to further study. All broadfields retained some of their

previous students, lost others and gained new participants to varying degrees. However,

examination of the relative size of the past and recent study cohorts of broadfields studied by

returning students revealed that some broadfields, for example science, lost participation and

reduced in size compared to the size of the past study cohort. Some, for example education, had a

net increase in size. Others had only slight gain or losses, but a lot of movement both in and out,

for example society & culture. Broadfields are categorised according to their change ratios (see

Table 4.6). By comparing the change ratios of individual broadfields, the broadfields (see Table

4.5) could be categorised into three groups (see Table 4.6).

` 86

Table 4.6

Groups of Broadfields

Group Change

ratio

Size of recent study cohort

compared with the past

study cohort

Colour

in Tables

6 & 7

Broadfields

Group 1 < -20% Recent study cohort < 80%

of past study cohort Blue

Science

Engineering

Creative Industries

Group 2 >20% Recent study cohort >120%

of past study cohort Pink

Business

Education

Health

Architecture/Building

Group 3 -20% to +

20%

Recent study cohort

between

-20% & +20%

Green

Society & Culture

Info Tech

Environ Science

4.2.1 Group 1: Broadfields with high negative change ratios. Three broadfields,

science, engineering and creative industries, experienced a loss of participation and the recent study

cohort for these fields reduced in size more than that of other broadfields when people returned to

tertiary study. In all three broadfields, the reduction came from two directions: the relatively larger

number of students who changed into another broadfield, that is, changed out of Group 1

broadfields, and the relatively smaller numbers who changed in compared with broadfields in

Groups 2 and 3.

Science. As shown in Table 4.6, 1,125 (66%) of those with qualifications in science

returned to study in a different broadfield, but only 230 changed from a different broadfield into

science (see Table 4.5). From the change ratio comparing the size of the recent study cohort and the

past study cohort in science, 52% fewer people completed recent qualifications in the broadfield of

science. Individuals with qualifications in science returned to university to study in broadfields

such as health (35%), business, (17%), society & culture (16%) and education (13%) (see Table

4.7). Numbers changing into science (230) were significantly below the overall change proportion,

(z = -11.1; p <.001), and numbers changing out of science (1125) were significantly above the

overall proportion changing (z = 14.8; p<.001) (see Tables 4.4 and 4.5).

` 87

Table 4.7

Changes out of Science

Broadfield

Number from science

who changed into

another broadfield

% of those changing

from science who went

into another broadfield

% of change-out

Cohort

Health 391 35.0 (391/1125) 7.0 (391/5409)

Business 191 17.0 3.0

Society & Culture 178 16.0 3.0

Education 149 13.0 2.7

IT 68 6.0 1.3

Agricul and Env 67 6.0 1.3

Engineering 52 4.6 0.9

Total incl Arch and CI 1125 20.8

Engineering. In a similar pattern to science, 58% ((728-308)/728) of those with

qualifications in engineering changed out of the broadfield. Both the proportion changing out (58%

of the past study cohort) and that changing in (30% of the recent study cohort) were significantly

different from the mean change percentage of 48% (p<.001). The change ratio of - 40% suggests,

as with science, that respondents with qualifications in engineering returning to study, tended to

study in a different broadfield from engineering. Of those who changed out, half completed

qualifications in business, 12% studied society and culture, 9.5% studied IT and 8% studied science

(see Table 4.8). A small proportion moving into society and culture from both science and

engineering may have indicated some disjunctive change.

` 88

Table 4.8

Changes out of Engineering

Broadfield

Number from

Engineering who

changed into another

broadfield

Percentage of those

changing from

Engineering who went

into another

broadfield

Contribution to

change-out Cohort

Business 217 51.0 4.0

Society & Culture 49 12.0 0.9

Science 35 8.0 0.6

IT 40 10.0 0.7

Total (incl other

broadfields) 420 7.8

Creative Industries. As indicated by a change ratio of -20%, creative industries had the

smallest proportional loss in Group 1 and there appeared to be considerable movement both into

and out of the broadfield with 65% of the past study cohort changing out and 56% of the recent

study cohort being made up of people who changed in. Generally the narrow-fields of this

broadfield contain two main areas, the performing and physically creative arts such as music, drama

and art and the verbally creative art of written communication which includes journalism.

4.2.2 Group 2: Broadfields with high positive change ratios. The broadfields of business,

education, health and architecture gained participation and increased in numbers, thus appearing to

retain returning students better than other broadfields and to attract more students whose past study

was in other broadfields.

Business. Business had a positive change ratio of 27%, with 61% of previous students

returning to again study business. Only 39% of those who had studied business before chose to

study in another broadfield. Just over half of the recent study cohort of business had previous

qualifications in a different broadfield. Both change-in and change-out proportions were

significantly different from the average of 48% (p<.001).

The broadfield of business attracted returning students from a wide range of broadfields

including 51% of those changing from engineering, 49% of those changing out of IT, 28% of those

changing from society and culture, 26% of those changing from health and 18% and 17% of those

changing from science and education respectively (See Table 4.9). Of those who changed out of

business, 43% went into society and culture, and 22% into education.

` 89

Table 4.9

Changing Field into Business

Broadfield

Number

changing into

Business

% of those changing

out of another

broadfield into

Business

% of the

Change-

into

Business

Contribution to change-

in Cohort

Society &

Culture 398 28.0 32.4 7.3

Engineering 217 51.3 17.7 4.0

Science 191 17.0 15.5 3.5

Health 118 25.6 9.3 2.2

IT 107 49.0 8.7 2.0

Education 82 17.5 6.7 1.5

CI 70 18.0 5.7 1.3

Total (incl

Arch and Env) 1,225 22.6

Education. The change ratio of 68% indicated that education was the broadfield that gained

the most in numbers of people returning to study. Only 34% of those choosing recent study in

education had a past study qualification in that discipline, so that 66% of the recent study cohort

was made up of students new to the broadfield (see Table 4.5). Of those who had studied education

previously, 57% chose further study in education and 43% changed into another broadfield. As

with business, those changing into education came from a spread of broadfields with 44% changing

from society and culture and a further 44% changing from a combination of business, science,

creative industries and health (See Table 4.10). With respect to changing out into other broadfields,

49% changed into society and culture and 18% into business.

` 90

Table 4.10

Changing Field into Education

Broadfield

Number who

changed into

Education

% of those

changing out who

went into

Education

% of the change-

into Education

% Contribution

to change-in

Cohort

Society &

Culture 538 38.0 44.0 9.9

Business 161 22.1 13.2 2.6

Science 149 13.2 12.2 2.7

CI 140 35.9 11.6 2.6

Health 130 28.2 11.0 2.5

IT 31 14.2 2.6 0.6

Engineering 25 5.9 2.1 0.5

Total (incl Arch

and Env) 1207 22.2

Health. Whereas the broadfields of both education and business corresponded to a higher

than overall change proportion for participants changing in and lower for people changing out,

health, though with a lower change ratio of 24%, was significantly below the mean for both change-

in and change-out. The change-in proportion of 43% and the change-out proportion of 31% were

both significantly lower than the overall change proportion of 48% (See Tables 42 and 4.3). Of

people with past study in science, 34.5% changed into health and comprised almost half of those

changing into health (See Table 4.11). Of these, 27% changed into medicine.

` 91

Table 4.11

Changing Field into Health

Broadfield

Number who

changed into

Health

% of those

changing out

who went into

Health

% of the

change into

Health

% Contribution

to change-in

Cohort

Science 391 34.5 49.0 7.1

Society & Culture 179 12.6 22.8 3.3

Business 94 12.8 11.9 1.7

Education 55 11.5 6.9 1.0


CI 24 6.2 3.1 0.4

IT 11 5.1 1.4 0.2

Total (incl Arch

and Agri 794 14.7

Architecture and building. This was one of the two smallest broadfields with a past study

cohort of only 210. However, the change-out proportion at 28% was the lowest of all broadfields.

Though the proportion changing in was not significantly different from the overall 48% (see Table

4.2), the high change ratio of 41% placed this broadfield in Group 2. Because of the small size of

the broadfield which indicated it was not chosen by many students, analyses of broadfield

movements of changers both in and out were not considered further.

4.2.3. Group 3: Broadfields with relatively low change ratios. In the Group 3 broadfields

of society & culture, information technology and environmental studies, low change ratios did not

indicate that there was very little change of broadfields associated with the Group. Rather, the low

ratios were a consequence of the proportions changing in being similar to the proportions changing

out for each of the three broadfields, so that the nett change was low. Interestingly this occurred in

two of the three smallest broadfields, IT and environmental studies, and in the largest, society and

culture, so it was unlikely that this outcome was simply a consequence of the broadfield sample

size.

Agriculture and environment. With a past study cohort of only 188, this was the smallest

broadfield but with a change proportion of 71% in both directions and a change ratio of only 1.6%,

it had the largest proportions of respondents changing in and changing out. Agriculture and

environmental science was the only broadfield where the number changing in from another, single

broadfield (67 from science) was greater than the number staying in the broadfield (55). However,

the numbers in the broadfield overall were very small and because of this further analysis of related

patterns of change were not undertaken.

` 92

Information Technology (IT). The change patterns with information technology were

different from the other STEM Fields of science and engineering. A very low change ratio of 3%

showed that the numbers changing into IT were only 3% higher than those changing out. There was

considerable movement both into and out of the broadfield with a change-out proportion of 66%

and a change-in proportion of 67%, but thus a very small overall net change in absolute numbers.

The past study cohort and the recent study cohort differed only by ten. Differences from the

overall no change/change proportion of 48% were significant for movement both into and out of IT

(see Tables 4.2 and 4.3). The interesting feature of the IT broadfield was that 32% of the past study

cohort changed into business which was nearly half of those who changed out of IT. Those who

changed in came from science, engineering, business and society and culture.

Society and Culture. The broadfield of society and culture was the largest by number of

participants and arguably the most diverse of the broadfields. The low change ratio of -12% was

the result of considerable movement into and out of the broadfield, with 1,410 or 46% of the past

study cohort changing out and 1,043 or 40% of the recent study cohort composed of participants

who had changed in (See Tables 4.4 & 4.5). The 40% change-in proportion was significantly

different from the overall change proportion of 48% but the change-out proportion of 46% was not

(See Tables 4.2 &4.3)

Of those who changed out, 38% went into education. This percentage together with the

numbers that moved into society and culture from education, suggest a perceived association

between these two broadfields. Nearly one-third (28%) changed from society and culture into

business and 13% changed to health. Society and culture attracted almost half of those who changed

out of education and 42% of those who changed out of business. Together these two groups made

up 51% of those who changed into society and culture (see Table 4.12).

` 93

Table 4.12

Changing Broadfield into society and culture

Broadfield

Number who

changed into

Society &

Culture

% of those

changing out

who went into

Society &

Culture

% of the

change-into

Society &

Culture

Contribution to

change-in Cohort

Business 308 42.5 29.4 5.7

Education 229 48.9 21.9 4.2

Science 178 15.9 17.0 3.3

Health 125 26.5 12.0 2.3

CI 84 21.5 8.0 1.5


IT 29 13.3 2.8 0.5

Total (incl Arch

and Agri 1043 19.2

4.2.4 Summary of findings on patterns of broadfield change. Several concepts were

described to aid data analysis. From this approach it was found that change-in/change-out patterns

differed widely between broadfields. Some broadfields, such as business and education, were much

more likely to be chosen by people returning to further tertiary study than others such as science

and engineering where numbers reduced significantly. From these change patterns, broadfields

could be categorised into three groups according to whether they lost or gained in numbers or

maintained the status quo. The movement of respondents returning to study appeared to be away

from fields of science and technology into social science and business.

4.3 Patterns of Change Between Broadfields and Selected Narrow-fields.

As discussed in Chapter 3, the broadfield of society and culture contained a diversity of

professional qualifications in fields such as law, psychology, social work, and archaeology, and

courses in various other speciality areas such as history, geography, languages and theology.

Therefore, an analysis was conducted on the narrow-field classifications within society and culture

to examine possible movement between narrow-fields within the broadfield as well as between

these narrow-fields and the other major broadfields. Without such an analysis, changes that may

have happened between what are perhaps popularly seen as different professional areas would have

been missed.

4.3.1 The narrow-fields within society and culture. The proportion changing between the

society and culture narrow-fields at 63% was significantly greater than the 48% overall change

proportion (z = 11.84; p <.001), however, this figure could also be inflated by the feature of the

questionnaire discussed below.

` 94

Of the 13 narrow-fields within society and culture, no further detailed analysis was

undertaken in political science, justice, library and museum, philosophy and religion, criminology

and family studies, sport and recreation because the numbers in these courses were very small.

Some, such as political science, and language and literature, had much greater than 100% increase

in numbers in the recent study cohorts, but again this apparent high percentage change-in was likely

to be a result of the major in the past study not being specified, as most of the change-in came from

one of the two generic degrees (See Table 4.13).

A problem arising from the questionnaire. As indicated in Table 4.13, a dominant

movement with the broadfield of society and culture appeared to be from the generic degrees of

Bachelor of Arts and Bachelor of Social Science into most other narrow-fields. Seven hundred and

forty-one of the 1,578 respondents with past study in the generic degrees did not change broadfield,

but almost 99% of the 741 changed within the society and culture broadfield to areas of

specialisation including law, psychology, welfare, library and museum, language and literature,

religion, criminology, and family studies. Such a large apparent movement may have been greatly

inflated because respondents were not required to nominate their major field of study in their past

qualification, but were required to do so in their recent qualification. Hence a degree such as

Bachelor of Arts as a past qualification stated without speciality could only be coded with the

generalist society and culture broadfield code (09), but a recent qualification could be given the

code of the particular narrow-field studied within society and culture such as psychology, literature

etc. Thus there were only nine respondents who received the generic ‘09’ code in recent study. To

facilitate analysis, a matrix (Table 4.13) was prepared of past and recent study in the narrow-fields

within society and culture.

` 95

Table 4.13

Past and Recent Study Matrix showing the numbers of individuals with Past and Recent Study in the

narrow-fields of society and culture

Narrow-fields

Past Study →

0 1 3 5 7 9 11 13 15 17 19 21 99 Total

Recent Study ↓

0. BA/B.Soc. Studies 6 0 0 0 0 2 0 1 0 0 0 0 0 9

1.Political science 79 9 10 4 0 4 0 1 2 0 11 0 2 122

3. Social Studies 164 1 25 10 6 4 0 6 5 1 6 0 1 229

5. Welfare 68 1 13 74 37 0 2 1 2 6 4 0 0 208

7. Psych 114 0 9 7 264 0 0 1 3 4 5 0 0 407

9. Law 109 3 9 3 3 149 15 2 2 2 19 1 1 318

11. Justice 18 0 0 0 3 5 8 0 1 0 1 0 0 36

13. Library 36 0 3 2 2 0 0 8 5 0 0 0 0 56

15.Lang & Literature 93 1 5 2 2 3 0 1 18 1 0 0 1 127

17. Philos. & Religion 32 0 4 2 1 3 0 1 2 14 0 0 0 59

19. Economics 7 0 2 0 0 0 0 0 0 0 31 0 0 40

21.Sport & Recreation 0 0 0 0 0 0 0 0 0 0 0 4 0 4

99.Crime & Family 15 2 3 1 2 2 0 0 0 0 0 0 6 31

Total 741 17 83 105 320 172 25 22 40 28 77 5 11 1,647

Key to narrow-fields of society and culture:

0. Arts and Social science 1. Political science 3. Studies in Society: E.g. History, Geography,

Anthropology, Sociology, Archaeology 5. Welfare 7. Behavioural science, Psychology 9. Law 11.

Justice 13. Library and Museum 15. Language and Literature 17. Philosophy and Religion 19.

Economics and Econometrics 21. Sport and Recreation 99. Criminology, Family Studies and

Security

` 96

Table 4.14

Larger narrow-fields (NF) of Society and Culture and Change/No Change

Field

Past

Study

Cohort

Recent

Study

Cohort

Change

ratio

Number (%)

not changing

Change-in

From

BA/

B.Soc.Sc

Main Broad or

narrow-field

changed into

(% past study

cohort)

BA/

B.Soc.Sc 1,578 15 10,400% 9 (.6) N/A

Psych* 455 581 28% 264(58) 114

8.8 Edu

9.2 Heal

7.7 Bus

Law 240 566 136% 149 (62) 109 15.0 Bus

6.7 Edu

Economics 190 83 -129% 31(16) 7

42.0 Bus

10.0 Law

7.0 Edu

Welfare* 174 371 113% 74(43) 68

16.0 Edu

13.0 Heal

7.0 Bus

In the larger narrow-fields of society and culture, many respondents stayed within society

and culture but some of these changed their study major. For example, 71.7% of the law past study

cohort remained within society and culture, but 13.3% of those changed into a different narrow-

field such as political science or justice. However, with most of the larger narrow-fields, except

economics, people with past study in the field did not change field at all. This applied to 62% of the

law past study cohort, 58% of the psychology past study cohort and 43% of the welfare past study

cohort (see Table 20). The sizes of both the past and recent study cohorts for these larger narrow-

fields can be compared in Table 4.13. The comparison between the size of the past study cohort

and the recent study cohort for each narrow-field are highlighted by the change ratios which ranged

widely in size (see Table 4.14). The numbers studying law and welfare more than doubled in the

recent study cohorts and halved in Economics.

Column 6 (Changing in from BA/BSoc.Sc) in Table 4.14 lists the numbers that changed

into the narrow-fields from the generic degrees. The extent to which the coding problem discussed

earlier could be falsely adding to the change numbers would depend on particular narrow-fields. It

is unlikely that Australian universities would offer a major in law in a generic degree, therefore it is

probable that changes from an arts or social science degree to law are actual narrow-field changes.

` 97

Table 4.15

Broadfields Past Study Cohorts and Recent Study in narrow-fields of Society & Culture

Broadfields of Past

Study→ 1 2 3 4 5 6 7 8 9 10 Total

Society and culture

narrow-fields of

Recent Study ↓

0 BA./B Soc. Sc 2 0 0 0 0 2 1 1 9 0 15

1 Political science 5 1 1 5 1 4 6 23 122 11 179

3 Society Studies 13 1 4 1 5 6 19 21 229 17 316

5 Welfare 37 2 3 1 3 24 59 26 208 8 371

7 Psychology 67 2 7 2 0 38 27 23 407 8 581

9 Law 24 10 11 5 9 22 20 138 318 9 566

11 Justice 0 1 2 0 0 1 12 13 36 1 66

13 Library 7 4 2 0 0 3 11 7 56 11 101

15 Lang & Literature 14 3 4 2 0 13 29 13 127 15 220

17 Philos/Religion 1 3 3 0 0 5 38 8 59 3 120

19 Economics 3 2 5 2 3 3 1 24 40 0 83

21 Sport 2 0 0 0 0 5 2 3 4 0 16

99 Crim/Fam 3 0 7 1 1 0 4 8 31 1 56

Total 178 29 49 19 22 125 229 308 1,647 84 2,690

Note. Key to the broadfields of past study cohorts: 1. Science, 2. Information Technology,

3. Engineering, 4. Architecture and Building, 5.Agriculture and Environment, 6. Health, 7.

Education, 8. Business, 9. Society & Culture, 10. Creative Industries

` 98

Table 4.16

Narrow-fields of Past Study in Society & Culture and Broadfield Recent Study Cohorts

Society and culture

narrow-fields of

Past Study→

0 1 3 5 7 9 11 13 15 17 19 21 99 Total

Broadfields of

Recent Study↓

Science 25 0 1 3 6 0 0 1 0 0 4 0 0 40

IT 18 1 2 1 4 1 1 2 1 0 4 0 1 36

Engineering 6 0 0 0 1 0 0 0 0 0 1 0 0 8

Architecture 21 0 3 0 1 4 0 1 0 1 1 1 1 34

Agriculture & Env 19 3 1 2 0 1 0 1 0 0 1 0 0 28

Health 77 3 9 23 42 4 1 1 1 2 4 11 1 179

Education 359 2 23 27 40 16 7 5 23 14 14 4 3 538

Business 196 5 14 12 35 37 2 8 3 3 79 3 1 398

Society & Culture 741 17 83 105 320 172 25 22 40 28 77 5 11 1647

Creative Industries 116 2 3 1 6 5 0 4 7 0 5 1 1 151

Total 1578 32 139 174 455 240 36 45 74 48 190 25 19 3057

Changes between the narrow-fields of society and culture and other broadfields. Of the

1,578 respondents with generalist degrees in the social sciences, 53% changed out of society and

culture and 43% of those changed into education, 23.5% into business and 14% into creative

industries. Column 7 in Table 4.14 lists the percentages of the narrow-field past study cohorts that

changed into various broadfields. For example, 15% of the law past study cohort changed into

business and 6.7% into education. Most of the changes were into education, health or business. It

is interesting to note from Table 4.16 that 138 (24.4%) of the law recent study cohort was made up

of people who changed in from business.

In what could be considered add-skilling, 69.3% (79) of those with a degree in economics

changed field to add a business qualification. Again numbers are relatively small, but within those

changing from a psychology qualification, 85% went into either education (29.6%), health (31%) or

business (26%).

Of all the changes into society and culture, numbers changing into law were higher than into

any other single narrow-field (248 or 23.7% of the changes into society and culture). Overall, those

with previous qualifications in society and culture predominantly did not choose scientific or

technical fields, with only 48 choosing science or engineering and 36 choosing IT, out of a total of

1,420 who chose to change fields of study.

4.3.2 Narrow-fields within other broadfields. As mentioned above, the level of

professional diversity in narrow-fields of society and culture, is not present to the same degree in

` 99

other broadfields where, it could be argued, the narrow-fields are generally perceived by the

community as belonging to one general professional area. For example, disciplines such as

medicine, nursing, dentistry and allied health in the broadfield of health are generally concerned

with healing, and accounting and management in the broadfield of business are concerned with

business or finance issues. However, a brief examination was undertaken of change-into the

narrow-fields of the health, business and education to extend understanding of how individuals are

structuring their careers.

Health. In the broadfield of health, 70% of the past study cohort did their recent study in

health and of those, 60 % also stayed within their same narrow-field. This continuing study in the

same speciality area applied particularly to nursing (73%) and allied health (63%). Other speciality

areas such as dentistry and veterinary science had even higher retention rates, but the numbers

involved were very small. The changes that occurred within health were primarily into two narrow-

fields: public health, including occupational health and safety (31% of changes in), and medicine

(22%). Interestingly, public health was also the narrow-field with the greatest percentage of

changes out, with 58% of the past study cohort choosing to study in a different narrow-field.

Within the context of movement between broadfields, 60% of those whose past study was in

health and in the narrow-field of Nursing, had recent study in that same speciality. The retention

rates of other narrow-fields were not as high, with 41% for allied health and 25% for public health.

However, there were no particular patterns of change from health narrow-fields into other

broadfields. As mentioned earlier, there was notable movement from the broadfield of science into

health. Of those, 40% changed into medicine, 14% in public health and 13% into allied health.

Nursing, with high retention rates, only attracted 12% of those who changed into health.

Business. As established earlier, 22.6% of the total number changing into a new broadfield

went into business. Of those, 67% went into the narrow-field of business management, including

74% of those with past engineering degrees who changed into business, 68% of those with past IT

qualifications and 65% of those who past study was in science.

Education. Of those who changed into education, 91% studied teaching of which 39%

specialised in secondary teaching, including 37% of those who changed in from society and culture

and 51% of those who changed in from science. A further 24% went into primary teaching, but the

actual percentages of teaching type remain a little unclear as a further 18% did not specify the level

of teaching studied. Sixty-eight percent of those who changed into the narrow-field of teaching

from society and culture came from the narrow-field containing the generic qualifications of

Bachelor of Arts or Bachelor of Social Science.

` 100

Thus in the larger narrow-fields of health, nursing had the highest retention rate, but did not

attract many people changing their broadfield. The speciality of business management was the

most popular for those changing into business and, predictably, teaching attracted most of the

people changing into education.

4.4 Chapter Summary

Importantly, fields of past and recent study were found to be statistically related in the

following way. There was a marked difference in proportions of people changing into and out of

particular broadfields. STEM fields of science and engineering showing marked net loss of

participants when people engaged in further study, while both business and education made large

gains in numbers. The largest broadfield of society and culture and the relatively small field of IT

showed a balance between gain and loss. The broadfield of health was had the lowest proportions

changing in and out.

Patterns of change between some pairs of broadfields were observed, proportionally the

largest of these being from engineering to business where 51% of those with previous study in

engineering made the change. Education was generally a popular choice for further study, but no

distinction between change-into primary or change-into secondary education was made in the

analysis. Any relationship between these patterns of change and contextual variables, particularly

age, will be discussed in the following chapter. The interviews in Phase 2 of the present study

provide interesting insights into motivations for particular patterns of choice, and to how those

patterns are viewed by individuals in terms of whether or not the change is seen as disjunctive.

` 101

Chapter 5

Change of Broadfield in Context

In the previous Chapter, the results focussed on the extent to which respondents had

changed their broadfield of study in their choice of recent study programs, and the patterns of

change in relation to particular broadfields. Dominant patterns of change were identified. To

improve understanding of the reasons for these change choices, this chapter reports results related to

how these broadfield change patterns were related to demographic and contextual variables. Age

was of particular interest because of the relationship between age and occupational mobility

identified in Chapter 2.

The research question addressed here is Question 3: Were staying or changing patterns related to

particular demographic or contextual factors?

The patterns of change are examined from the perspective of age alone and also in

association with other variables such as gender, methods of course payment and whether study was

full time or part time. Initially, the Chapter reports on the age composition of the sample and the

distribution of age groups across broadfields of recent study. Next, the association between age and

broadfield change is analysed with respect to categories of broadfields and to the dominant patterns

of broadfield change noted in the previous Chapter. The relationship between age and broadfield

change is then considered in relation to other contextual variables of gender, pay type, and study

mode (full or part time study). The significance of each of the four categorical contextual variables

as predictors of the dependent variable, change/no change, are tested using multiple logistic

regression analysis and the resultant odds ratios discussed.

5.1 The Relationship between Participant Age and change or No change of broadfield

5.1.1 The age distribution of the sample. Most job and occupational change occurs before

age 30 (Huang & Sverke, 2007; Kambourov & Manovskii, 2008; 2010). The respondents selected

in this project had returned to enrolment for study in a second tertiary qualification which may have

been related to intended occupational change. Therefore it was expected that there would be a

concentration of respondents with ages less than 30 years.

The age range of the sample was 19 to 79 with a median age of 31. Thus the distribution

was heavily skewed towards the 19 to 30 age range in keeping with expectations. The frequency of

a return to study by age declined sharply from the mid-twenties to age 65 followed by only single

digit frequencies to age 79 (See Figure 5.1). These results are in keeping with Neal’s (1999)

findings that workers made fewer and less complex job changes as they aged.

` 102

Therefore in the sample, returning to further study appeared to be related to the age of the

respondents. The next section of this chapter looks at whether age is also associated with the

broadfield of further study.

Figure 5.1. Age Distribution of the Sample

The use of age groups. As age 30 appeared to be the age below which most occupational

change occurred (Huang & Sverke, 2007; Kambourov & Manovskii, 2008; Savickas et al., 2009),

ages to 29 were combined as the first group, where most broadfield change was expected, if

broadfield change is a correlate of occupational change. From there, for the purposes of

comparison, remaining ages were grouped in ranges of 10 years until age 59, with those 60 and

above forming one group only because of the very low numbers of respondents (see Figure 5.2).

The comparative size of the five age groups can be seen in Figure 5.2.

` 103

Figure 5.2. Frequency of respondents by age group

5.1.2 Age groups and broadfields. To investigate the possibility of a relationship between

age and choice of broadfield, matrices of age groups and the numbers in broadfields of both past

and recent study were constructed. The percentages of respondents by age group in each broadfield

past study cohort and recent study cohort were then calculated. Table 5.1 and Table 5.2 contain the

percentages of age groups in each broadfield from an age group perspective, respectively, while

Table 5.3 and Table 5.4 present the age group percentages of each broadfield past study cohort and

recent study cohort respectively. A Chi-squared test for the association of age groups and

broadfield past study cohorts demonstrated that the age group of respondents returning to further

study and the broadfield of their past study were not independent (Chi-squared (44) = 1,100 p <

0.001). For example, over 50% of the respondents with past qualifications in science (58%),

architecture (75.8%) and creative industries (59%) who returned to study were aged less 30,

compared with only 15.9% of those with past study in education.

Group1:

Group 2:

Group3:

Group 4:

Group 5:

0

1,000

2,000

3,000

4,000

5,000

6,000

19-29 30-39 40-49 50-59 >59

Fre

quen

cy

Age Groups

Frequency of age groups

` 104

Table 5.1

Distribution of Age Groups across Broad Felds in the Past Study Cohort (%)

Age

group Past Study Cohort of all Broadfields

↓ Sc IT Eng Arch Agri Heal Edu Bus S&C CI Approx.

Total

< 30 20.5 2.8 6.1 3.3 1.5 11.9 3.6 16.0 26.8 7.4 100

30-39 13.7 3.8 8.3 0.9 2.0 14.3 9.2 17.2 26.4 4.2 100

40-49 8.5 2.7 5.6 0.7 1.5 15.6 17.3 18.8 25.7 3.4 100

50-59 6.4 1.5 3.5 0.8 1.3 15.3 24.0 13.3 31.3 2.7 100

> 59 8.2 1.6 3.8 0.0 1.6 9.8 20.1 9.2 39.7 4.9 100

Table 5.2

Distribution of Age Groups across Broad Felds in the Recent Study Cohort (%)

Age

group Recent Study Cohort of all Broadfields

↓ Sc IT Eng Arch Agri Heal Edu Bus S&C CI Approx.

Total

<30 10.0 2.7 4.9 3.9 1.6 16.7 12.6 18.3 24.1 5.0 100

30-39 6.0 3.6 4.4 1.8 2.0 16.1 15.3 27.0 20.3 3.2 100

40-49 3.5 3.1 2.4 1.3 1.4 15.1 22.6 21.2 25.6 3.4 100

50-59 4.4 2.5 1.2 1.3 1.1 15.6 23.6 14.3 31.6 4.4 100

> 59 5.3 2.1 1.1 0.0 1.6 5.8 14.3 8.5 49.2 12.8 100

` 105

Table 5.3

Percentage Age Group Composition of Past Study Cohort of Each Broadfield (%)

Age

Group Broadfield of Past Study

↓ Sc IT Eng Arch Agri Heal Edu Bus S & C CI Mean

< 30 58.1 40.1 40.5 75.8 39.4 37.4 15.9 41.1 42.1 59.1 42.5

30-39 27.5 38.9 38.9 14.2 37.2 32.0 28.9 31.4 29.4 23.9 30.9

Up to 39 85.6 79.0 79.4 90.0 76.6 69.4 44.8 72.5 71.5 83.0 73.4

40-49 9.9 16.0 15.2 6.6 15.4 20.1 31.4 19.9 16.6 11.3 17.5

Up to 49 95.5 95.0 94.4 96.6 92.0 89.5 76.2 92.4 88.1 94.3 90.9

50 - 59 3.5 4.2 4.5 3.3 6.4 9.3 20.5 6.6 9.5 4.2 8.2

Up to 59 99.0 99.2 98.9 99.9 98.4 98.8 96.7 99.0 97.6 98.5 99.1

>59 0.9 0.9 1.0 0.0 1.6 1.2 3.4 0.9 2.4 1.5 1.6

Approx.

Total 100 100 100 100 100 100 100 100 100 100 100

Table 5.4

Percentage Age Group Composition of Recent Study Cohort of Each Broadfield (%).

Age

Group Broadfield of Recent Study

↓ Sc IT Eng Arch Agri Heal Edu Bus S&

C CI Mean

< 30 60.5 39.0 53.8 65.9 43.1 45.4 34.0 37.9 43.2 50.8 43.5

30-39 25.1 35.6 33.4 21.4 35.9 30.1 28.6 38.6 25.0 22.7 29.6

Up to 39 86.0 74.6 87.2 87.3 79.0 75.5 62.6 76.6 68.3 73.5 73.3

40-49 8.3 17.5 10.1 8.7 14.3 16.1 24.1 17.3 18.0 13.6 14.8

Up to 49 93.8 92.1 97.2 96.0 93.3 91.6 86.7 93.8 86.2 87.1 90.3

50-59 5.0 6.8 2.4 4.0 5.1 7.9 11.9 5.5 10.5 8.4 6.7

Up to 59 98.8 98.9 99.6 100 98.4 99.4 98.6 99.4 96.7 95.4 98.4

>59 1.9 1.1 0.4 0.0 1.5 0.6 1.4 0.7 3.3 4.6 1.5

Approx.

Total 100 100 100 100 100 100 100 100 100 100 100

The age group percentages in the broadfield recent study cohorts were further evidence of

the apparent trend for particular broadfields to attract younger students. For example, in science

(60.5%), engineering (53.8%) and in architecture (66%), over half of the respondents had

completed their recent study by age 30. The percentage completing their recent study in these

` 106

broadfields before age 40, at 86%, was well above the overall recent study cohort average of 73%

for that age range. However, in education, only 62.6% of the recent study cohort were aged less

than 40 suggesting that people are more prepared to study education at an older age than they are to

study science and engineering. Similarly the percent completing recent study in society and culture

under age 40 was 68%. Information technology was more on a par with business and health, that

is, close to the mean of 73% for the people completing their recent study under age 40 (see Table

5.4).

A statistical Chi-squared test established that there was a significant association between the

age of people completing further tertiary study and the broadfield of study undertaken (Chi-squared

(40) = 640.33, p <0.001). Table 21 also shows that 20% of the youngest age group had their past

study in science, yet the science past study cohort made up only 15% of the overall past study

cohort.

There was a significant relationship between the broadfields of both past study and recent

study and the age group of people returning to further tertiary study which raised the question of

whether age was also related to changing broadfield, in particular to the patterns of change noted in

the previous chapter.

5.1.3 Age groups and change of broadfield. A matrix was prepared for age groups and the

change/no change variable. The percentages changing and not changing for each age group are

presented in Figure 5.3. Using a Chi-squared test of association it was found that changing

broadfield was significantly related to the age group of the respondent (Chi-squared (4) = 111.30,

p<0.001). The median age for change at 32.5 years was slightly higher than the median age of the

whole data set. From Table 5.4, it can be seen that the 37.5% of broadfield change-in the overall

recent study cohort was in the under 30 age group, and 70.6% in age groups 1 and 2 combined.

However, as age group 1 included 45% of the total sample, and age groups 1 and 2 included 76.4%

of the sample, they were proportionally under-represented in the individuals changing broadfield.

Further, logistic regression used to analyse the relationship between change and age groups

revealed odds ratios that reinforced an interesting feature of Figure 5.4. The proportion of each age

group making a change increased with increasing age such that 42.5% of age group 1 changed

broadfield, which increased to 52.6% of age group 2, and to 53.1% of age group 3. Change in

broadfield for age group 4 was a lower 47.5% but was still higher than for age group 1, and age

group 5 had the highest proportion of changers at 55.4% (see Figure 5.3 and Table 5.5). As shown

in Figure 5.2, the number in each age group was progressively smaller with increasing age, so that

though the proportion changing increased with age, the actual numbers decreased considerably.

` 107

Thus fewer older people returned to study but those that did were more likely to change their

broadfield of study than their younger counterparts.

Table 5.5

Age groups and change: Odds ratios

Age groups Odds ratios Standard Error z P>z 95% confidence.

interval

2 1.50 .07 8.98 0.000 1.37 1.64

3 1.53 .08 7.95 0.000 1.38 1.70

4 1.23 .00 2.82 0.005 1.06 1.41

5 1.68 .25 3.44 0.001 1.25 2.26

p< 0.05

Figure 5.3: The Percentages Changing and not Changing Broadfields by Age Group

In the previous chapter a relationship was established between broadfield change and

particular broadfields, and in this chapter between age and broadfield of both past and recent study,

and between age and changing broadfield, which raises the possibility of a three-way interaction

between age and the said patterns of broadfield change. The next section considers this possible

interaction.

5.1.4 Age and changes into all broadfields. As discussed in Chapter 4, each change was a

change both out of one broadfield and into another broadfield. Comparison of the age groups of

those who changed into each broadfield recent study cohorts revealed considerable variation in the

age pattern between broadfields. Several had marked differences between proportions of age

` 108

groups 1 and 2 while others had almost identical contributions from both younger age groups. For

example, those changing into the health recent study cohort were made up of 52% from age group 1

and 27.6% from age group 2, while those changing into science came in equal proportions from

both age groups 1 and 2 (both 37%) (see Table 5.6). Additionally, there were differences in the

proportion of older age groups changing into various recent study cohorts.

Table 5.6

Age Group make-up of Recent Study Cohorts with Change of Broadfield (%) (Change-in)

Age Sc IT Eng Heal Edu Bus S & C CI

Proportion

of whole

Recent

Cohort

Changing

% to 29 37.0 33.0 39.0 52.0 42.0 27.2 32.6 40.0 37.5

% 30 to 39 37.3 37.2 39.6 27.6 28.0 46.5 25.4 26.9 33.1

% up to 39 74.0 70.4 79.0 80.0 70.0 73.7 58.0 67.0 70.6

% 40 to 49 14.7 19.5 16.8 13.0 21.0 19.8 25.2 15.3 19.4

% up to 49 89.0 89.9 95.0 93.0 91.0 93.5 83.2 82.0 90.0

% 50 to 59 8.6 8.8 3.8 7.0 8.0 5.7 12.5 11.6 8.2

% up to 59 98.0 98.7 98.8 100.0 99 99.2 95.7 93.6 98.2

% over 59 2.2 1.3 0.8 0.5 1.2 0.8 4.3 6.7 2.0

The health, engineering and science recent study cohorts were younger overall then those of

society and culture, and education. People changed into society and culture and creative industries

at later ages than for the other broadfields with only 58% of changes into society and culture being

completed by age 39 compared with approximately 80% for health and engineering (See Table 5.6).

Business, and to a lesser extent IT, stand out as different from all other broadfields. A

much higher proportion changed into those fields between 30 and 39 years than before age 30

(46.5% compared to 27.2% for business) (See Table 5.6). All other broadfields experienced the

highest influx from individuals in age group 1. Thus age, broadfield, and change of broadfield are

interrelated in the current analytic sample. In the next section, the relationship between the

dominant patterns of change and age, established in the previous chapter, will be examined first

from the perspective of broadfields and broadfield groups that lost or gained participation per se,

and then between particular pairs of broadfields.

5.1.5 The relationship between age groups and groups of broadfields. In the previous

Chapter, broadfields were divided into three groups according to whether or not they lost (group 1)

or gained (group 2) participation when people returned to further study, or whether they neither

` 109

gained nor lost (group 3). In this section the variable age is shown in relation to changes out of and

into the Groups of broadfields described above. For convenience, Table 4.6 from Chapter 4 is

repeated here as Table 5.7, with the three different groups of broadfields colour coded in keeping

with the previous chapter:

Blue: broadfields that lost participation

Pink: broadfields that gained participation

Green: broadfields with no overall change-in participation

Figures 5.4 and 5.5 show the broadfield changes from the perspective of each age group. In

these figures, the broadfields are colour coded as per Table 5.7 to ease comparison of possible

difference in change patterns between different groups of broadfields in relation to age groups.

Unfortunately, the differences in broadfield size confound this comparison to an extent, but the

age/change patterns between the larger broadfields are notable. Neglecting the smaller broadfields

of agriculture and environment, architecture, and information technology there appears to be some

consistency with each group with regard to age-related changes, with occasional exceptions. For

example, the relatively high proportional change-in to creative industries in the over 59 age group

diverges from the group 1 pattern. Consistency within group 3, the neutral change group is difficult

to assess because the group contains two small broadfields and the largest broadfield.

Table 5.7

Groups of broadfields

Group change

ratio

Size of recent study

cohort compared with

the past study cohort

Colour

in

Tables

6 & 7

Broadfields

Group 1 <-20% recent study cohort <

80% of past study cohort Blue

Science

Engineering

Creative Industries

Group 2 >20%

recent study cohort

>120% of past study

cohort

Pink

Business

Education

Health

Architecture/Building

Group 3 -20% to

+ 20%

recent study cohort

between

-20% & +20%

Green

Society & Culture

Info Tech

Environ science

` 110

Figure 5.4. Percentages of Changers in Each Age Group Changing OUT of all Broadfields.

` 111

Figure 5.5: Percentages of Changers in Each Age Group Changing INTO all Broadfields

` 112

Age related change and group 1 broadfields (negative change ratio):

science, engineering and creative industries. The graphs in Figure 5.4 showed a tendency for the

changes out of the group 1 broadfields (in blue) to be undertaken at younger ages than changes out

of broadfields with positive and neutral change ratios ( in red and in green). Approximately 75% of

the movement out of science and engineering broadfields occurred before age 40.

From Figure 5.5, the comparatively low proportions of each age group who changed into

group 1 broadfields are obvious and consistent across age groups, for science and engineering.

Though absolute numbers were very small, the highest proportion of any age group changing into

creative industries was group 5, (over 59) which may be suggestive of later life recreational activity

than career change.

From Chapter 4, science and engineering showed a marked loss of participants when

individuals engaged in further tertiary study and combined losses made up a disproportionate 27.2%

of the whole change-out. With science, age group was significantly related to changing out (Chi-

squared (4) = 44.66, p <0.001) and 83% of individuals who changed out came from combined age

groups 1 (53%) and 2 (30%) (see Figure 5.5).

Similarly with engineering there was a significant relationship between age and changing

out (Chi-squared (4) = 80.18, p < 0.001), but the pattern differed from science in that the highest

proportion of changes out occurred in age group 2 (44.7%) (see Figure 5.6.) Though still

predominantly in the younger groups, changes out of engineering seemed to have a wider spread

across age groups than science, but at 51% of changes out of engineering the dominant change

pattern from engineering was into business. The change-out pattern for creative industries was

similar to that of science.

` 113

Science (n =1120)

Engineering (n = 423)

Creative Industries (n = 391)

Figure 5.6. Percentage Age Group Composition of Changes out of Science, Engineering, and

Creative Industries

.

` 114

Figure 5.7. Numerical Changes into and out of Science

Similarly with engineering there was a significant relationship between age and changing-

out (Chi-squared (4) = 80.18 p < 0.001), but the pattern differed from science in that the highest

proportion of changes-out occurred in age group 2 (44.7%) (see Figure 5.6). Though still

predominantly in the younger groups, changes-out from engineering seemed to have a wider spread

across age groups than science, though the dominant change pattern from engineering was into

business (51%). The change-out pattern for creative industries was similar to that of science.

Thirty-five percent of those changing out of science went into health and 70% of these were

aged below 30 (see Figure 5.8). Of the 13% who changed into education and the 16% who changed

into society and culture, nearly 50% were in age group 1. However, of the 17% who changed into

business, over 50% were between 30 and 39 (see Figure 5.9).

` 115

Figure 5.8. Percentages of Change from Science to Health by Age Group

` 116

Figure 5.9. Percentage Age Group Composition of Changes INTO Education, Business, and Health

` 117

Age groups and changes into and out of broadfields with a positive change ratio:

education; business; health and architecture. The change-in patterns in relation to age for the

broadfields of education and business were similar to the patterns of change-out for science and

engineering. For education, as Figure 5.9 shows, the highest proportion of change-in (42%) was in

the youngest age group, but for business it was in the 30 to 39 group (see Figure 5.9) as previously

discussed. This pattern for business differed from all other broadfields except the much smaller

broadfield of IT which showed a similar but less marked trend (see Figure 5.16). Business and

education together received 45% of those changing in, but the age of change-in varied.

Compared with the eight largest broadfields, health had the lowest proportions changing out

(31%). Of those that changed in, over 52% came from the under 30 years age group, which was

higher than for any other broadfield. The proportions changing in from other age groups decreased

steeply as age increased (Figure 5.8).

Age and dominant patterns of change INTO business. The overall proportion of other

broadfields changing into business differed between broadfields, with the highest proportion

moving from engineering (51%) as discussed. However, there was very little difference between

broadfields in the age at which the change into business was undertaken. From engineering,

science, health and society and cultures between 40% and 50% of the changers into business were

in age group 2 (see Figure 5.10).

Age and changes from various broadfields INTO education. The age groups of those

changing into education from individual broadfields can be seen in Figures 5.11, 5.12, and 5.13.

The age range from different broadfields varied considerably. Thirty-six percent of those changing

out of creative industries went into education and of those, 60% were in age group 1 (see Figure

5.12). A similar percentage (38%) of those changing out of society and culture went into education

but just less than 40% were from age group 1 (see Figure 5.11). However the first three age groups

were equally represented at about 30% each in people changing into education from business (see

Figure 5.13).

` 118

% From Engineering (n =216) From Science (n= 190)

% From Society and Culture (n = 398) From Health (n = 118)

Age Groups Age Groups

Figure 5.10. Percentage Age Group Composition of Changes INTO Business

` 119

Figure 5.11. Percentage Age Group Composition of Changes from Society and Culture INTO

Education

Figure 5.12. Percentage Age Group Composition of Changes from Creative Industries INTO

Education

Figure 5.13. Percentage Age Group Composition of Changes from Business INTO Education

` 120

Age groups and changes to broadfields with a neutral change ratio : society and culture;

information technology; and environmental science.

Change into and out of society and culture. Change into society and culture, the major

neutral change broadfield, showed less variation across Age groups than other broadfields (see

Figure 5.14). The society and culture recent study cohort had a smaller proportion of people from

age group 1 (32%) changing in than any other broadfield. Further, age groups 1 and 2 accounted for

only 58% of the change-into society and culture, the lowest for all broadfields. Almost 17% of

those changing in were aged over 49 years compared with the average for broadfields of

approximately 10%. Society and culture was the most popular broadfield in the older age groups.

Creative Industries had a similar pattern in the older groups with 18.3% of those changing in

coming from age groups 4 and 5, but it showed a net loss of participants overall. Proportions

changing out of society and culture at various age groups have been discussed above in relation to

various other broadfields. As with the age patterns of changes into this the largest major broadfield,

there was less variation across age groups in change-out (see Figure 5), particularly when compared

with changes out of STEM fields in the Group 1 Broadfields.

Figure 5.14. Percentage Age group Composition of Changes IN and OUT of Society and Culture

In comparison with other narrow-fields of society and culture, law, with 248, received the

highest proportion (23.7%) of the changes into that broadfield. In relation to age, 66% of changes

into Law were completed by age 39, and 88% by age 49 (See Figure 5.15). Psychology also

received a high proportion of people from age group1 (42%; See Figure 5.15). Changing into both

` 121

law and psychology was significantly related to age group. The other popular narrow-field of

society and culture was welfare which had a median age for changing in of 40.5 years compared to

psychology (32.5) and law (34.5), suggesting that welfare appears to be seen as a viable study

change option for mature age students (see Figure 5.15). However, the relationship between age

groups and changing into welfare was not significant.

` 122

Figure 5.15. Proportional age group Composition of changes INTO narrow-fields

Information technology and agriculture environment studies. The other two broadfields in

Group 3, IT and environmental studies, both had high change-in and high change-out proportions.

The relationship between age groups and change or no change was not significant either for

numbers changing in or out (see Figure 5.16 for IT).

` 123

Figure 5.16. Proportional Age Group Composition of Changes IN to and OUT of Information

Technology

5.1.6 Age and pre-planned programmes. The inability to identify pre-planned

programmes which could inflate the estimate of occupational transition or disjunctive change was

mentioned in Chapter 3. In particular there are two common joint qualification programmes offered

at Australian Universities that have typically consisted of a Bachelors Degree followed by either

another Bachelor’s degree as in the postgraduate qualification for medicine or by a Graduate

Diploma as in a teaching qualification. Both programmes lead to registration in their respective

fields. While these joint programmes can be undertaken at any age, they are typically undertaken

immediately following high school, which would mean completion of the Graduate Diploma of

education by 21-23, or of the medical degree by age 25-27. The following table presents the

proportions and numbers of respondents who have changed and completed a second qualification in

education or in medicine in those age ranges as an indication of the possible degree of influence on

the overall change results.

` 124

Table 5.8

The Numbers and Percentages of 21-23 year olds changing into education

Age

No. of age

peer

changers

No.

Changing

into

Education

% of age peers

Changing into

Education

No. of age

peers in

sample

% of age peer

cohort Changing

to Education

21 66 16 24 292 5

22 183 61 33 591 10

23 251 90 36 706 13

Table 5.9

The Numbers and Percentages of 25-27 year olds changing into Medicine

Age

No. of age

peer

changers

No.

Changing

into

Medicine

% of age peers

Changing into

Medicine

No. of age

peers in

sample

% of age peer

cohort Changing

to Medicine

25 293 77 12 619 12

26 273 78 29 619 13

27 257 75 29 568 13

5.1.7 Summary of the influence of age. Returning to obtain an additional tertiary

qualification decreased markedly with increasing age. However, the tendency to choose a broad-

field of study that was different from that of the highest previous qualification actually increased

with age. Choice of broadfield and age group were not independent. The broadfields that were

grouped together according to change ratios in the previous chapter showed similarities with respect

to the ages that changes in and out were undertaken. Thus the patterns of change appeared to have

varied in accordance with an interaction of age groups and broadfields. In the following section, the

influence of these two variables in conjunction with the role of gender is examined.

5.2 Gender

The male/female ratio in the sample of data that met selection requirements was

approximately 40/60, which differed significantly from 50/50 (z = 22.61 p <0.001). This difference

in gender composition of the sample suggested that females engage in further study one and one

half times as often as males or, alternatively they are more likely to complete post study

questionnaires.

5.2.1. Gender, age groups and changes of broadfields. The gender ratio (male/female) in

change was 21/29. The proportional difference between males and females in tendency to change

` 125

broadfields was significant (p < .001). The odds of males changing broadfield were 1.19 times

greater than for females, but females generally made changes in broadfields at younger ages than

males (see Figures 5.17 & 5.18). Thus females were 1.5 times more likely to return to study but

males were 1.19 times more likely to study in a different field.

Figure 5.17. Age, Gender and No change (NO)/change (CH)

There was also a gender difference in broadfield preference and in the direction of

broadfield changes, with male choices suggesting a pathway from science and engineering courses

into business and females moving from society and culture to education and to some extent out of

science. Of the males who changed, 31% changed into business and of those, 53.1% came from the

STEM past study courses of science, IT and engineering, and 27.7% from society and culture.

Nearly 70% of the changes into IT were made by males. In comparison, only 16.3% of women

changed into business, but 27.9% changed into education which attracted only 14.4% of the males

(see Table 5.10).

From the graphs in Figures 5.19 to 5.23, the interaction of age group, gender and broadfield

under conditions of change can be readily seen. Dominant features of these graphs are the low

levels of change-into science and engineering courses, particularly by females, the 40% of changes

by males aged 30 to 39 years into business (Figure 5.24), and the increasing popularity of education

and particularly society and culture with increasing age. The standout feature of Figures 5.21 and

5.22 are the relatively high percentages of males (35%) and females (50%) in the oldest Age group

that changed into Society and culture.

0

10

20

30

40

50

60

UP TO 29 30-39 40-49 50-59 >59

%

age groups

MALES NO

MALES CH

FEMALES NO

FEMALES CH

` 126

Figure 5.18: Age Group Percentages and Gender with Change of Broadfield

` 127

Table 5.10

Percentages of Males and Females Changing into all Broadfields

Gender Broadfields

Sc IT Eng Arch Agri Heal Edu Bus S&C CI Total

Males

Frequency 122 157 100 63 59 276 327 713 363 81 2,263

% of Male Group

(Row)

5.4 7.0 4.4 2.8 2.6 12 14.5 31.6 16.1 3.5 100.0

% of Changes into

Broadfield (Col)

53.3 69.3 76.3 43.2 43.4 34.6 27.1 58.1 34.9 29.7 41.8

Females

Frequency 108 70 31 83 77 518 880 512 680 189 3,151

% of Female Group

(Row)

3.4 2.2 1.0 2.6 2.4 16.3 28 16.3 21.6 6.0 100.0

% of Changes into

broadfield(Col)

46.8 30.7 23.7 56.9 56.6 65.4 72.9 41.9 65.1 70.3 58.2

Total 230 227 131 146 136 794 1,207 1,225 1,043 270 5,409

Chi-squared(10) = 443.4235 (p < 0.001)

` 128

Males (n = 764) Females (n= 1,274)

Figure 5.19. Changes INTO broadfields under 30 years

Males (n = 857) Females (n = 929)

Figure 5.20. Changes INTO broadfields Age 30-39 years



0

10

20

30

40

50

Sc IT Eng Health Educ Bus Soc Sc CI

0

10

20

30

40

50


30-39

0

10

20

30

40

50


Proprortion of AgeGroup

0

10

20

30

40

50


Proporton of AgeGroup

0

10

20

30

40

50


0

10

20

30

40

50


` 129




Figure 5.23. Changes INTO broadfields Ages over 59 years

Thus the relationship between the Age and pattern of broadfield change was found to be

influenced by gender, with males showing a strong preference for changing into business

particularly between ages 30 and 39, while the change for females was spread between between

education and society and culture. Cost has been identified as an issue when individuals

contemplate a return to study (Neapolitan, 1980), so in the next section the possibility of a

relationship between age, choice of broadfield and course fees / methods of fee payment is

considered.

5.3 Fee Payment Options

In the questionnaire, four ways of meeting course costs were considered: Research Student

fees, Higher education Contribution Scheme (HECS) deferred some or all, HECS paid up-front, and

Australian fee-paying student. The cost of courses to the student and the payment schedule at

government funded (public) universities in Australia is determined by the fees-type each student is

0

10

20

30

40

50


0

10

20

30

40

50


0

10

20

30

40

50


0

10

20

30

40

50


` 130

eligible for, which is usually determined by their academic record, the amount of previous study

completed and the level of the course being studied. HECS funding is government assisted but the

student must contribute to the cost either by an upfront payment or by deferring payment in a

government loan scheme. Other students can pay much higher fees as Australian fee-paying

students. These fees must usually be paid up front. Research student fees are usually fully paid for

by the Commonwealth Government. It is important to note that course work higher degrees, such

as Masters degrees are usually not eligible for HECS funding, but student loans are available from

the Commonwealth Government.

The two most frequently used methods of payment were ‘HECS deferred’ and ‘Australian

fee-paying student’ together almost equally accounting for 78.3% of respondents (see Table 5.11)

5.3.1 Summary Statistics

Table 5.11

Frequency of Types of Fees

Fee Pay-Types

Frequency

%

“APA or RTS research student"

1,046

8.92

“HECS deferred some or all"

4,734

40.35

“HECS paid upfront"

1,504

12.82

“Australian fee-paying student"

4,449

37.92

` 131

Figure 5.24. Percentage age group Composition of Fee Pay-Type Categories with change

5.3.2 Course fees and payment methods in association with age groups. From Figure

5.24, the proportion of each age group using HECS funding decreased as age increased and

conversely, the number of Australian full-fee paying students increased with increasing age. People

in the highest age group, which is the over 59 age group, did not continue this tendency, but the

numbers were very small. Age groups and pay types were not independent (Chi-squared (12) =

477.67, p< 0.001), and changing or not changing was significantly related to Pay-Type (Chi-

squared (3) = 147.54 p < 0.001).

5.3.3 Methods of paying for the course and broadfields. In addition to the age factor in

choice of fee type, the broadfield variable also appeared of some relevance to the pay type chosen.

Under conditions of both change and no change, the broadfields of business and IT had the highest

proportions of Australian full fee paying students. Of returning students in business, 62% paid full

fees, and this rose to 63.84% with broadfield change. The percentages for IT were 52.69% and

59% respectively. Thus the variable age in combination with individual broadfields and pay type

was related to change or no change of broadfield.

5.4 Full-time/Part-time Study

It is possible that a decision to return to study and choice of course could also be affected by

the study mode of course offerings at universities, especially for returning students who may

already be working full-time. In this section the relationship between age, broadfield and mode of

study is explored. In the sample, the difference in overall proportion studying full- and part-time

was not significantly different from 50% (z =1.97, ns).

` 132

5.4.1 Full-Time/part-time study and change of broadfield. In the overall recent study

cohort, there were significant differences between broadfields in relation to the full-time/part-time

study variable, both in conditions of change of broadfield and of no change of broadfield. The Chi

squared results were Chi-squared (10) = 500.34 (p< 0.001) where there was broadfield change, and

Chi-squared (9) = 567.82 (p < 0.001) for the unchanged condition.

Comparing the proportions of full- to part-time study in Figures 5.25 and 5.26, there was no

consistent tendency across broadfields in relation to change/no change of broadfield. In some

broadfields, the proportion of full-time study was markedly higher where there was no change of

broadfield, e.g., science, engineering, creative industries and architecture and in others the opposite

was the case, for example, education and health. Business and IT were the only two fields where

the proportion of part-time study was greater with both change and no change of broadfield.

Figure 5.25. Percentages of Full-Time/Part-Time Study where Broadfield was Not Changed

Figure 5.26. Percentages of Full-Time/Part-Time Study where there was a Change of Broadfield

` 133

Figure 5.27. Percentages of Full-Time/Part-Time Study by Age-group with No change of

Broadfield

Figure 5.28. Percentages of Full-Time/Part-Time Study by Age-group with Change of Broadfield.

5.5.2 Age and full/part time study. From Figures 5.28 and 5.29, the percentage of each

age group who studied full-time decreased and the percentage of part-time students increased with

increasing age irrespective of whether or not the broadfield was changed. There was a slight

variation to this tendency in the oldest age group in the no change condition, but numbers in that

situation were quite small. The odds ratios (see Table 5.12) verify this observation. Individuals

were increasingly likely to study part time with increasing age. Those aged 50 to 59 chose part time

study 9 times more often that those aged < 30. Thus the choice of study mode was associated with

the age of the student and the particular broadfield chosen for study.

` 134

Table 5.12

The Relationship Between Age and Full Time/Part Time study

Age-adjusted odds ratios from logistic regression for full-time relative to part-time study

Full/-part Odds ratio Standard

Error z P>z 95% Conf. Interval

Category

age

2 4.94 0.23 33.76 <0.001 4.50 5.42

3 7.89 0.48 34.37 <0.001 7.02 8.88

4 9.19 0.77 26.61 <0.001 7.81 10.82

5 7.55 1.27 12.03 <0.001 5.43 10.50

_cons 0.37 0.01 -31.83 <0.001 0.34 0.39

` 135

5.5.3 Gender and full/part-time study. Where there was no change of broadfield, males

and females did not differ significantly in the choice of full or part time study. However, males

were significantly more likely to study part-time when they were changing their broadfield than

when not changing (z = 3.93; p < 0.001). This difference was not present for females.

Figure 5.29. Gender and Full /Part time study with change of broadfield.

5.5 Chapter Summary

This chapter focused on the variable of age in relation to changing or not changing

broadfield of study. Contextual variables were considered in their interaction with age to further

clarify the issues that may be associated with course choices and thus career development in various

age groups. Whether participants returned to study to change broadfield or to continue in the same

field as previous study, appeared to be significantly associated with the age group of the participant,

and the age group in interaction with gender, the particular broadfield of past and recent study, the

fee-paying method employed and the mode of study chosen.

` 136

Chapter 6

Results of Phase 2: The Qualitative Study

In Phase 1, quantitative data on patterns of broadfield study in graduates who had at least

two university qualifications at Bachelor level or above was examined, and the results reported in

Chapters 4 and 5. In Phase 2, qualitative interview data from nine participants who had each

undertaken extra credentialing in a field different from that of their initial Bachelor Degree was

used to investigate the rationale for and experience of changing broadfields, and the results are

reported in chapters 6 and 7, addressing Research Questions 4 and 5 respectively. With nine

interview participants, the qualitative data is not intended to focus on particular broadfield change

patterns, but to provide insights into the experience of undertaking occupational change between

knowledge occupations. The present chapter begins with a brief introduction to the stories of each

participant so that the results can be contextualised. The data-driven thematic structure is then

displayed, followed by a presentation of the main themes, themes and sub-themes that relate to

Question 4, illustrated by relevant quotes from interview transcripts.

6.1 An Introduction to the Story of each Participant.

(For further privacy, each participant is identified under a pseudonym.)

Sue initially wanted to study medicine, but chose psychology when told by her family GP

that specialisation in medicine was not appropriate for a female. After graduating with her Bachelor

of Science with a double major in Statistical Mathematics and Psychology, she completed an

Honours Degree in Clinical and Organisational Psychology and took a clinical position in a long-

stay mental health facility. However, she was very discontented with the position and her level of

expertise, and undertook further brief study in counselling, then a Masters of Applied Psychology to

widen her employment options. Following years in clinical and organisational practice in the

Public Service, she and her husband formed their own management consultancy through which she

became the breadwinner and successfully supported the family for many years. Her work involved

law-related dispute resolution and mediation, and she commenced a Juris Doctor part-time at age

52, to progress her career in mediation. At the time of the interview, she was continuing as the

breadwinner with a small clinical private practice, some HR work, and was still exploring avenues

into law.

Kate was unsure of her study preferences when she began University and eventually decided

on a Bachelor of Arts in film and television, even though she knew that finding related employment

would be difficult. For the best chance for success, she studied at UCLA for a semester with two

very successful work placements in film studios in Los Angeles. However despite excellent results

` 137

and references, she was only able to get volunteering and poorly-paid intermittent contract work.

After three years she commenced a degree in Veterinary science, and on graduation undertook

several internships to specialise. At interview she was working in a mixed practice (large and small

animals) near a large city, and was struggling financially.

Lettice graduated from her chosen field of law and moved to work in a small law firm,

where her large case-load focussed exclusively on emotionally-charged family law matters with no

mentoring. As relocation and a different firm did not improve her situation or equip her to move

into other fields of practice, she was psychologically and medically advised to leave after three

years. She then studied business full-time majoring in Finance to become an actuary, but was

advised the training was too long. Also, because of her partner’s work, she had to find employment

in Queensland, though many graduate positions in Finance were interstate. She took a graduate

position in a Government Department where she is very happy. Much effort has been made to

utilise both her fields of expertise.

Flynn was a successful science student at school and followed his brothers into engineering.

However, engineering did not engage him emotionally, so after one year he began an arts degree in

economics and political science part-time for interest only. After five years in engineering he

received a redundancy payment and a Commonwealth Treasury scholarship, and completed the last

two years of the degree full-time, with promise of a graduate position in Treasury. He was

interested in policy, extended his economics and political science studies with a Masters in Public

Policy over four years, and moved between several state treasuries gaining promotion to a

management position.

Fay chose her initial degree field of Human Movements from her subject preferences at

school, with little idea of outcome options. However, mature-aged students were offered the few

full-time graduate positions available, and Fay took several part-time positions to gain maturity,

experience and expertise. After two exhausting years of several concurrent part-time jobs, Fay left

fitness, worked in insurance for three years, and tried several other fields of work. Following a

relative’s suggestion she then completed a 1-year Graduate Diploma of Education (Primary). On

graduation she found vacancies were scarce. After some volunteering and relief teaching, at the

time of interview (2½ years after graduation from education) Fay had a contract with a

disadvantaged school, and was enjoying her work. She felt she was fortunate to have had contacts

within the profession to get her initial position.

Cheryl wanted to work in health and enjoyed her Podiatry degree. However, after

graduation, she was unfortunate in her choice of practice positions, was subjected to forms of

workplace harassment and after one year decided to move into a different field. She began a

` 138

Masters of Business part-time externally, then swapped to health management. With the masters

partially completed, she again changed podiatry jobs, to a positive working environment better

salary, and at interview was incorporating her administration knowledge with clinical practice. Her

chances of buying into that practice may be limited by her partner’s occupational need to relocate,

but she fells her qualifications are very portable.

Keith. A constant thread through Keith’s narrative was that he had always wanted to study

medicine, but was concerned by the level of responsibility, the time required and the financial

aspects. He had not considered outcomes on choosing his initial Bachelor of Arts, and tried several

options on graduation before deciding to follow his family into teaching. After 10 years teaching,

he commenced the MBBS which had been reduced to a 4-year postgraduate course. Keith married

during the course and became a father. After graduation he surrendered an option to specialise

because of child-care and financial considerations, with some regrets.

Melanie began a degree in radiography for entry to medicine but felt uncomfortable in

hospitals, changed to commerce/engineering, and again to a Bachelor of Pharmacy on the advice of

the manager in a pharmacy where she worked part-time. On graduation, the employment situation

for pharmacists had deteriorated markedly, and after completing her registration year, she move to a

country town interstate for full-time work. On returning to Brisbane, she increased her hours

gradually but disliked pharmacists’ working conditions, the monotonous work, and lack of career

structure. After family and friends’ reassurances about job outcomes, Melanie began an

Accounting qualification almost full-time externally while working nearly full-time, which was

very demanding. At the time of interview she was partway through her programme.

Brian was interested in science and robotics, completed an engineering degree in

mechatronics after school and worked in Engineering Design for four years. His mother and sister

are both in medicine which Brian considered offered the fulfilment, meaning and social contribution

that engineering for him did not, and he decided to change. With no biology knowledge, he had to

study hard for the medical entrance examination. At the time of interview Brian was just

completing second year, was thoroughly enjoying the course and practical work, and contemplating

a speciality choice.

To focus information on participant’s broadfield changes, Table 6.1 summarises the

particular broadfield change (and if appropriate, narrow-field) undertaken by each of the

participants.

` 139

Table 6.1

Participants’ Broadfield Changes with Narrow-fields Indicated

Participant Broadfield of Initial Qualification Broadfield of change

Sue society and culture (NF: Psychology) society and culture (NF:

Law)

Kate Creative Industries (N F:Film and Televisions) health: (NF: Vet science)

Lyn society and culture (NF: Law) business (NF: Finance)

Flynn Engineering

society and culture (NF:

Economics and Political

science)

Fay health (NF: Sport and Recreation) education: (NF: Primary

Teaching

Cheryl health (NF: Podiatry) business (2 NF: Finance.

health Administration)

Keith education (NF: Secondary) health (NF: Medicine)

Melanie health (NF: Pharmacy) business (NF: Accounting)

Brian Engineering health (NF: Medicine)

Table 6.2 presents the ages of commencement and completion of the first qualification for

all participants, and the age of commencement of the change qualification. These age figures

indicate the time that lapsed before a decision was made to change, and the life-stage of each

individual at the time of the change action. The age at interview compared with the age at the

commencement of the change qualification indicates the time each had to evaluate the outcome.

` 140

Table 6.2

Age at Commencement of Initial and change Degrees and at interview

Participant

Age 1st

Qualification

commenced

Age 1st

Qualification

completed

Age change

Qualification

commenced

Age at Interview

Sue 18 21 52 61

Kate 18 20 23 30

Lyn 18 21 24 41

Flynn 18 21 24 49

Fay 18 20 28 30

Cheryl 18 20 23 28

Keith 18 21 33 46

Melanie 20 23 27 27

Brian 18 21 26 27

Note: Highlighted rows indicate change qualifications ongoing

6.2 Thematic Structure

The data-driven analysis process undertaken was described in Chapter 3. The thematic

structure and interrelationships between main themes, themes, and sub-themes can be seen in Figure

6. 1. Four main themes (see Figure 6.1) emerged from the interview data.

The main themes, Decision-Making Processes and Returning to Study, address Question 4

regarding the reasons for returning to study in a different broadfield, and are the focus of this

chapter. Individual-in-Situation addresses Question 5 on how the demographic and contextual

variables affected the decision to change field of study, and are presented in chapter 7. Outcomes,

as the fourth main theme name suggests, incorporates the end result of the broadfield change

process, and will conclude chapter 7. Beginning with Decision-Making Processes, all theme levels

are presented and supported with representative statements from the participants.

` 141

Figure 6.1: Thematic Structure

` 142

6.3 Main Theme: Decision-Making Processes

Ineffective initial decision-making processes were seen by many participants as contributing

to their later decision to change field of study. The main theme, Decision-Making Processes,

encompassed processes employed by individuals in both initial and change qualification selections.

Two themes, Consultation and Individual Approach, contributed directly to the main theme and will

be discussed in reference to both the initial choice of qualification and the change choice. Some

participants believed their level of personal maturity influenced their initial decision, as discussed in

chapter 7 under the theme Age.

6.3.1 Theme: Consultation. Consultation concerns the assistance participants sought from

others in making their broadfield choices, and is considered in relation to both initial and change

choices.

Sub-Theme: Making initial decision. All participants commenced their initial degree

immediately after high school. In choosing the broadfield of that degree, no participant sought one-

on-one career counselling with a qualified counsellor, even though several had one at their school.

Eight participants felt they knew what course they wanted, or had their own processes for making a

choice, and couldn’t see the relevance of a career counsellor. For example, Melanie explained that:

We had one (guidance officer). I think we did one thing … everyone did the test to tell you

what you want to do and then you go and have a half hour session. …in high school I knew

where I was going and so didn’t feel the need for assistance.

Similarly, Fay thought the school guidance officer “may be only for students who didn’t

know what they were going to do. But I had an idea so I didn’t even think to ask anyone at school,

teachers or anything.” She’d made her initial degree selection alone, as had Cheryl and Melanie.

However, after one year at university, Melanie was unhappy with her choice and sought assistance,

but had very little recollection of the discussion: “I did go to see someone at (university)… I don’t

remember too much of the specifics.” She eventually chose a field of study on the advice of her

part-time job manager.

Family and friends were usually the people chosen for discussion of initial career decisions.

Keith had spoken, “to my parents about what I should do.” However, the discussions were not

always supportive. Keith expressed an interest in medicine, but his father doubted his ability to

focus on the course. Similarly, Sue explained: “My mother wanted me to be a doctor…. I wanted

to be a specialist and she didn’t want me to be a specialist and took me along to the local GP who

said specialising was not suitable for females”. With this advice, Sue rejected Medicine and chose

psychology. Only Lettice, who had chosen law, sought clarification of the tertiary entrance

procedure from a teacher.

` 143

Sub-Theme: Making change decision. Participants sought more advice from others for the

change degree than for the initial degree, but again consulted family and friends whose opinions

they respected. Fay consulted her friends before trialling semi-skilled employment unrelated to her

initial qualification: “I told my best friend… she said it was a band-aid solution.” Fay later

discussed her employment situation with her brother- in-law, a teacher: “I told him I still didn’t

know what I was going to do, and he suggested it (teaching)…so I went home, looked up a few

things on the internet… and I was applying the next week.”

Melanie gathered as much information as possible from family and friends before starting

her change degree: “I asked everyone for advice. Told them what I was thinking and asked them

what they thought, given that they knew me. I took all the opinions on board and made my

decision.” Here Melanie’s reference to the importance of the opinions of people who ‘knew’ her

reflected her attitude to professional career counselling.

Interestingly, with three participants, the friends and family members consulted were

working in the fields they were considering or eventually chose. Only Lettice sourced opinions

from other professionals: her school maths teacher, a practising actuary, and later one of her

lecturers.

Three of the participants decided to change into a field they had previously considered.

Unhappy with the employment outcomes of their first degrees, Keith, Kate and Brian chose to

change into broadfields they had always been interested in, and received support from friends long

aware of their interests. Keith had been sourcing friends’ opinions about studying Medicine for a

number of years, and finally made the change when a close friend told the course had been

shortened to four years.

In summary, very little assistance was sought from professional career guidance personnel

in the decision-making process for the initial degree or the change qualification. However, family

and friends were consulted by most participants, though perhaps more enthusiastically for the

change choice.

6.3.2 Theme: Individual approaches. The theme Individual Approaches covers

participants’ own initiatives in choosing initial and change broadfields and courses.

Sub-Theme: Making initial decision. With the exception of Lettice, participants

demonstrated very limited attempts to consider their suitability for particular fields, or to research

course outcomes regarding the nature of the job, the work environment, and the availability of

degree-related employment. As discussed next, three slightly different methods were used to select

the initial broadfield of study. Three participants based their selection on their high school studies,

` 144

four focused on a professional area that interested them, and two engaged in a type of trial-and-error

approach.

Both Flynn and Brian, who went from science and maths into engineering, felt the choice of

broadfield followed almost naturally from their school studies, though in Flynn’s case there was

also an element of following his two brothers’ occupational choice. Fay also focussed on subjects

she had enjoyed at school (biology, Japanese, and business), as a basis for tertiary study choices, but

was unsure which she preferred. On her tertiary entrance application, she listed courses related to

her school studies, specifically human movements and international business, in order of the

university entry score required for each, as she was unsure of her personal preferences.

I was really just looking through the (tertiary entrance) book… in the different areas I was

interested. I liked science and Biology so I was looking through the courses to do with that.

I also did Japanese and business subjects, so the other avenue I looked at was International

business.

Thus she allowed the centralised tertiary placement system to determine the course she was

offered:

I got accepted into human movements with my OP [tertiary entrance score] so I just thought,

‘Ok. This is what I’ll do.’… It wasn’t just PE classes. I’d be learning about the human

body, and how it works and how to help people with it, so I thought that could be the thing

I’d be interested in. For the first two years though I just went through the course not really

knowing what I was doing, just learning. It was in the last year that I realised that this was

what I could do as a career.

Four participants had a definite idea of the occupation or field they wanted to go into which

was not necessarily related to their high-school subjects. For example, Cheryl had decided on the

health field, Sue on psychology, Melanie on medicine and Lettice on law. Cheryl wanted to work in

health but avoided professions her school friends chose because of the competitive nature of the

group. She had looked at course outlines but “… I honestly didn’t think too much about where I

was going with my Bachelor Degree.” Again, there was no real focus on outcome. Like Fay,

Cheryl allowed the tertiary entrance system to determine her health speciality: “On my QTAC form,

[Queensland Tertiary Admissions Centre] I pretty much filled out the courses in terms of their

required entrance rank. Whatever had the highest OP to get in, I put that first.”

In hindsight, Sue was not happy with the amount of research she had undertaken into the

broadfield of her initial degree: “I didn’t really explore it well enough.” Melanie had decided on

medicine without having studied biology because: “I’d looked at everything …and wasn’t overly

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keen on anything else.” She had never been in a hospital, not even as a visitor. Medicine was a

graduate-entry course, and she chose radiography as a pre-medicine degree mainly because it was

only three years and she “wanted to get into medicine as soon as possible.” However during her

radiology practical at the end of first year, she found she “really just didn’t feel comfortable in the

hospital environment”, so she questioned “what am I doing going into medicine?”

Lettice was the exception in terms of seeking information about the career direction she was

interested in, which was law. With her mother, she “went to university open nights and things and

talked to people there.”

Despite initial field choices, three participants engaged in trial-and-error approaches related

to their initial degree. Kate, Melanie and Keith were all unhappy with their choice at some stage in

their degrees, and proceeded to adjust their plans several times. Kate’s trial and error began from

day one at university. She spoke a lot about plans, which she acknowledged underwent ongoing

change for at least the first year of university.

On day one my plan was the Bachelor of Information Technology (BIT) and the

Bachelor of Arts (BA) and I was going to do 2 majors in Arts which were going to be

psychology and journalism… the BIT was what was going to get me a job, and the BA was

just going to entertain me.

Her process of change is best described in her own words.

I went to lecture number 1 (journalism) and thought ‘not for me, no.’ Then I actually

trialled a few different things. I was going to major in maths for a little while because I

really enjoyed some of the IT maths subjects. And then second semester first year, I decided

I’d start a film and television course, so at that point I was doing IT, psychology and film

and television. At the end of the year, I realised I was not at all enjoying IT and I’d sort of

only chosen it because I thought that I’d get a job in it…. and I was loving my film course

so I dropped IT altogether and kept going with the BA, kept going with psychology, and

made film and television I guess my major in priority there.

After trialling several subject options, Kate chose a study area she enjoyed, despite knowing, from

her brother’s experience, how difficult it would be to find employment on graduation.

Similarly, after rejecting radiography and thus medicine after her first year at university,

Melanie enrolled in a dual Bachelor of Engineering/Bachelor of Commerce degree but didn’t

actually start engineering.

I was a bit lost at the time because I had been focussed on Medicine…I just wanted

something and I had always loved Maths and numbers at school…couldn’t see myself

without a degree… I think I was just trying to cover all bases at the time.

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Melanie found Commerce “quite boring”, and in that year obtained casual work in a pharmacy.

She found the manager’s praising of pharmacy as an ideal profession especially for a woman,

persuasive, thought the work was interesting, and worked hard at university to get high grades in

her commerce program to upgrade to Bachelor of Pharmacy the following year. Thus she did not

commence what was her initial degree until her third year at university.

Keith had a slightly different trial-and-error story. Like Melanie, he had an early idea he

wanted to do medicine. After year 12, however, he told his parents he “wanted to be a professional

golfer,” but felt he “had to go to university and do something.” With a vague plan, he also allowed

the QTAC system a role in his course selection: “So I thought I’d put arts down because I knew I’d

get in …and thought if golf doesn’t happen, I’ll go back and be a teacher.” Both his parents and two

siblings were teachers: “I knew the lifestyle …and it would give me plenty of time for golf.”

However the backup plan was not immediately activated: “At the end of my Arts Degree,… I knew

that golf wasn’t a goer…I knew I didn’t want to be a teacher.” He embarked on a brief job search

and discussion with a stock broker who offered him a job and suggested some relevant part time

study. Keith did a semester of Economics, “found it very dry and boring”, worked for some months

as a clerk, and went on a holiday. He had “realised before I went on the holiday that I should have

done a Dip Ed,” and enrolled on return.

In summary, three participants acknowledged the role they allocated to the tertiary entrance

system to determine their initial choices. Flynn and Keith followed family members into the

broadfields of engineering and teaching respectively, believing they were well aware of the likely

outcomes and lifestyles of these initial choices. Others had difficulty finding a broadfield of study

that interested them and found themselves trialling several before deciding on an occupational area.

There was limited evidence of consideration of outcome pathways by some participants who

embarked on broadfields they knew little about.

Sub-Theme: Making change decision. Individuals tended to approach the change decision-

making process with a focus on the reasons for their dissatisfaction following their initial degree,

which contributed to their motivation for change.

All but two participants decided to return to study in a different broadfield because of

dissatisfaction with employment following their first degree. The reasons for dissatisfaction are

discussed in detail later under the Main Theme ‘Returning to Study.’ Of interest here is how that

dissatisfaction after the initial qualification affected their approach to choosing the broadfield they

would change into.

Fay, Melanie, and Kate cited limited availability of degree-related fulltime employment

following their initial graduation as a crucial aspect motivating their return to study. It was

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therefore surprising that Fay ignored the advice of an informed friend when considering a second

study in primary teaching: “A friend… told me that there may not be a lot of work there (in primary

teaching) but I didn’t worry about that too much.” Melanie and Kate explored employment

opportunities in the change broadfield each was considering. Melanie “was …worried …there are a

lot of accountants that come out of uni… and that I wouldn’t be able to get a job at the end of the

course, …I didn’t want to get myself into that again.” She had planned her approach to finding

employment in accountancy, but based her change decision on discussions with informed friends

rather than exploration within the industry.

Similarly Kate prioritised employment opportunities:

I think I was so sick of having to work so hard to find any job…. I saw this course (Bachelor

of Veterinary science) with something like a 98% employment rate for its graduates and it

was an area I was interested in, and I thought yeh that could work.

She had always been interested in being a Veterinarian, and disregarded negatives about the

profession that she was aware of, just as previously she had disregarded knowledge that work was

limited in her initial choice (film and television). Thus all three quickly focused on areas without

thoroughly exploring outcomes or other options.

Keith and Brian were unhappy with their initial degree-related employment for different

reasons and, like Kate, both turned to professions they’d always thought about. Keith had been

considering medicine since school and decided to change when the course length was reduced by

two years. Brian also followed an earlier interest in medicine when he became dissatisfied with the

social isolation he experienced in engineering. With both his mother and sister in medicine, he

knew and liked what he would study and was confident in outcome options.

Lettice, whose dissatisfaction and distress stemmed partly from gender-related work

allocation practices in some law firms, considered a degree in Finance and Funds Management.

However, she didn’t investigate practices in her proposed field to detect any employment pitfalls

she should avoid.

Two participants did not relate their change qualification to dissatisfaction or a need to

change occupations. Sue chose a change broadfield to ‘add-skill’ in her occupation, and selected

the particular course having considered overseas and interstate trends, and the courses on offer.

Despite negativity from her family, she chose to do a law degree, a situation which had much in

common with her approach to her original decision.

Flynn’s decision-making processes differed again. Because he had initially commenced the

‘change’ course for interest rather than occupational change, outcome options had not been an issue.

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When he took a redundancy package after six years in engineering, he was halfway through an

Economics and Political Science qualification, which he was enjoying:

I guess I didn’t say that I didn’t want to do engineering anymore… I didn’t have any grand

plans when I left my job of wanting to go into any particular career. I just wanted to do the

study for a couple of years, finish that off and then make plans.

In summary, the focus on school studies seemed no longer relevant in change decision-

making, and there was no evidence of trial and error in choosing a change broadfield. Some

participants selected a long-held professional field preference. Dissatisfaction with the availability

of employment from their initial degrees was the prime motivator for participants to return to study

in a different broadfield, but in many cases there was evidence of only cursory exploration of their

proposed new professions. Thus, for most participants, the decision-making process involved a

little more rigour when considering change (as opposed to the initial decision), with more

investigation of availability of employment.

6.3.2 Summary of the main theme: Decision-Making Processes. In both initial and

change decision-making, there was little evidence of consultation with career guidance

professionals. Instead, the opinions of friends and family were sought and relied upon. Various

rather haphazard methods were used to select initial broadfields and courses, so that personal

attributes and the nature and availability of employment outcomes were generally not considered in

any depth, though some followed family traditions and had reasonable expectation of the work.

Some participants changed into fields where they had a good understanding of the type and

circumstances of likely employment outcomes, and some took account of their personal aptitudes

for the change occupations. In their change decision, some aimed principally to avoid the

unsatisfactory outcomes from their initial choice, and focussed on the main aspect of their

dissatisfaction rather than exploring other likely outcomes of their change choice. With the

anticipation of the many costs of occupational change between knowledge occupations, it is

surprising that participants did not seek professional consultation.

6.4 Main Theme: Returning to Study

The Main Theme, Returning to Study, comprised two Themes: Reasons for Changing

broadfield and Choosing change broadfield. Findings related to these themes are presented next.

6.4.1 Theme: Reasons for changing broadfield. Participants’ stories about reasons for

studying a change qualification focussed on aspects of their initial university courses, but more

particularly on dissatisfaction with employment outcomes or other employment issues.

Sub-Theme: Dissatisfaction with initial degree content. Five participants considered that

their chances of successful employment outcomes from their initial course were limited by a

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perceived failure of tertiary educators to prepare them for particular aspects of practice: establishing

and working in professional relationships with clients, especially distress clients; and having a

sufficient level of expertise.

Lettice’s choice to study Law was well researched. The course material maintained her

interest throughout, but she was unprepared personally for the practice demands in her first job. She

felt the university had not attempted to equip students to cope with the emotional nature of family

law or the time demands of legal practice:

We did six months of Family Law and it’s just reading some cases, not the actual dealing

with a middle aged man sitting in your office crying his eyes out because he’s not going to

see his kids this weekend and that sort of thing. How was I going to deal with that? … I

think I had at one stage about 160 to 180 files that I was handling at any one time… I was

there at 6 or 7 in the morning, taking work home, working through lunch breaks and I just

had no life…. I’d be waking up in the middle of the night thinking that person is not going

to see their child because I didn’t get that letter off to their solicitor about their access.

Similarly, but with less personal impact, Cheryl felt the podiatry course “was very clinical,

which was fine…but you don’t realise the relationships you build with clients…what they expect

from you…that was quite exhausting in the first few years.” Cheryl also mentioned the high

dropout rate: “I think it is quite standard with podiatry. It will usually halve by the time you have

your graduating class.” Cheryl felt the university could have been more sensitive to the personal

development needs of younger students. “Generally anyone who was a mature-age student … knew

exactly what they were getting into. So there were more problems with people around my age.”

Fay made similar comments about her course in human movements, particularly about the

advantages mature-age students had. She too felt there was no attempt to address the issue of

dealing with professional trainer-client relationships. She felt mature-age students with life and

work experience coped more confidently in practical placements and were the employers’ first

choice on graduation. Fay became aware of her inadequacies: “When I was trying to deal with

someone and reassure them that I did know what I was speaking about, maybe I wasn’t coming over

as confident because I thought I didn’t really understand them.” To remedy the situation she chose

a psychology elective, which was also a negative experience:

I didn’t know how to write for psych. I only barely passed the subject and I don’t think the

lecturer was very happy with an Applied science / Human Movements girl trying to do her

subject. But I had all the prerequisites, so I was eligible to do it, however she made it quite

clear to me that she didn’t think I should be doing the course.

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There was also an element of general unpreparedness after graduation. Fay decided to specialise in

exercise physiology.

It was a very narrow-field and there wasn’t a lot of work for someone who was ‘green’ like

me. They (where she did her last practicum) were hoping to employ me after my practicum

but they said I didn’t have enough experience or knowledge to be confident in the role by

myself.

She took several casual jobs to gain experience in the field: “I had four or five jobs at one time. My

income wasn’t worth the time and the effort, getting sick all the time.”

Sue’s criticism of her initial course also involved inadequate training. She said “I decided

that the psychology training hadn’t been very effective, so I took a postgraduate miscellaneous

subject in counselling through the education department. ” Kate’s dissatisfaction about the film and

television course in Australia was content related: “writing essays about films, rather than thinking

about how to make movies or write movies.” She did an exchange at a university in Los Angeles

where she “got to be involved in film production….and was working for some casting directors …

in auditions, and dealing with actors and their agents and all that” via two internships.

The main bases for discontent with initial degrees were inadequacy in employment-related

skills and knowledge content, and a failure to prepare students for working in a professional

relationship with clients and for coping with the emotional and physical demands of practice.

There also seemed to be a disconnect between courses and labour market demand.

Sub-Theme: Dissatisfaction with employment outcomes. World of work issues that many

participants felt motivated their return to study were the availability of positions, the nature and

meaning of the work or the physical work environment, and human resource matters such as

occupational health and safety (harassment).

Dissatisfaction with the availability of positions. Several participants struggled to find full-

time employment in their initial degree broadfield. Fay’s situation was mentioned above, but she

also observed that: “There weren’t any exercise physiology jobs on the internet at that time, or if

there were they were up in Cairns or in Canada.” People with prior experience in the field got the

few available local positions.

Similarly Kate could find only short contracts in film and television in Australia despite her

high GPA and hands-on experience in Los Angeles. “The nature of the business is contract work.

You have a job for two months and then you have nothing for two months, just because the shoots

tend to be like that. ..Everyone’s job is like that.” The company she worked with in the USA would

have employed her but as there was no shortage of casting assistants, she was unable to get a visa.

Her time in LA was of some use: “That’s actually what got me jobs when I came back to Australia.

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It was because of the people I met there.” After much volunteering and several short paid contracts,

Kate eventually tired of having to work so hard to find any job, especially of having to constantly

phone people and self-promote.

When Melanie commenced her Pharmacy degree “it used to be super easy to get these

positions (preregistration)”, but when she graduated she saw that “it had changed and it changed

fairly quickly as well”. She was able to “do that (registration) year through the same Pharmacy that

I had been working at,” but after that it was almost impossible to get a full time hospital or

community pharmacy position, so she worked in Grafton for nine months. On returning to Brisbane

she still found employment difficult to obtain:

Now it’s even worse because there are so many pharmacists around it is even more

competitive…. I picked up two regular shifts for a start which was Sunday and a Monday so

it was enough to live on. I also picked up some locum work from some other pharmacies

and did that for a few months. Early the next year I got offered more regular shifts.

As she saw it, most pharmacists in community pharmacies seemed to be in similar positions. More

universities were offering pharmacy degrees, flooding the market with new graduates.

Relationship problems at work. For two participants, relationship problems with employers

contributed to their discontent with their professions. Lettice’s problems with family law have been

discussed, but were exacerbated by a bad experience she described as “conflict with the partner”, in

her first law firm. While Cheryl liked podiatry, she described her negative experiences: “In the first

job, I was the only female working for him, which wasn’t very good.” In another job, her boss “lost

all six staff members in a six-month period from harassment and bullying. So we all just got out.

We ran for the hills.” As Cheryl saw it: “a lot of workplace harassment and things like that …in my

first two or three years … is actually why I did further study...to get away from it.” While for Kate

harassment wasn’t a major issue, she mentioned that “the job with the television producer was kind

of horrible because he was not the nicest person.” For her that feeling generalised: “I found I didn’t

like the people (in the Film industry) a whole lot.”

In summary, several participants were disillusioned to discover on graduation that there

were very few positions available requiring their degree-related skills and knowledge. They

resorted to multiple casual positions, which was tiring and stressful, and no basis for financial

security. Others had to leave positions because of workplace harassment.

Sub-Theme: Disillusionment with the profession or the nature of the work. Many

participants expressed disillusionment with the initial degree-related occupation, or found they were

interested in the area but didn’t enjoy the practice. The sources of discontent varied between

different broadfields and related to the needs, values and expectations of the individuals involved.

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Flynn and Brian were dissatisfied with their Engineering work. Flynn liked engineering but

“didn’t feel it engaged me emotionally.” Brian described his experience of engineering:

I found it boring. It’s very impersonal, engineering. You spend all your time on the

computer and have the isolation of working on your own thing….but I didn’t feel it was

fulfilling. I didn’t feel that what I was going to do was ever going to impact anything.

Melanie’s dissatisfaction with pharmacy partly reflected the difficulty of obtaining

positions, but also the lack of career structure: “in community pharmacies you tend to hit a brick

wall. If it’s big enough you could become the pharmacy manager but once you go there that’s

pretty much all there is…no opportunity for promotion.” She disliked the mundane shop assistant-

type work and found the pharmacist’s work “loses its challenge after a few years of doing the same

thing”. In addition, staff management and complaint resolution were expected, without extra pay.

With changes to the Government subsidised Pharmaceutical Benefits Scheme, pharmacies are

apparently not as profitable as they had been but remain very expensive to buy, so Melanie had no

aspirations in that direction.

Fay was disillusioned with personal training work, though she liked the challenge of

helping people with conditions such as diabetes or osteoporosis:

In the degree they teach you to teach people so they can continue…looking after their

program themselves, whereas in personal training you have to make them rely on you so

they keep paying...it wasn’t my idea of helping. It was soul sucking.

Lettice was very unhappy with what she saw as a common practice in law firms to “pigeon-

hole” young female graduates in family law work, which effectively trapped them in what became

their main area of experience. She was offered no support and her large caseloads of emotionally-

charged “complex family and marital disputes, custody battles, and allegations of abuse” left her

“so worn out, getting sick, and emotionally a bit of a mess”. Moving home and changing firms did

not change her situation. Her attempts to move into a less emotional area of law were not

successful because of her lack of experience. “I’d applied for a few corporate type things but I

wasn’t getting any interviews.” One firm offered her a position in criminal law but: “I was leaving

family law for the emotional issues and I didn’t really want to get into the criminal side.” The

decision to leave legal practice was “quite sad actually” as she had “certainly enjoyed the Law

Degree”.

From a resource perspective, Sue was disillusioned in her first position of psychologist in a

long-stay mental health facility:

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It was terrible. I hated it. And I decided also that the organisational factors and the

architectural factors influenced the clients’ behaviours because the place was originally set

up as a tuberculous facility. It was never designed as a psychiatric hospital. That influenced

lots of things. You also had charge nurses there who had been through the war so that they

had issues. I decided that it wasn’t for me.

Keith acknowledged he’d accepted teaching because of the lifestyle it allowed. However he

found progression in the teaching hierarchy meant increasingly there “was always stuff to do”, so

the demands began to encroach on his lifestyle, and he decided to make a change.

The majority of participants were disillusioned with the nature of the work or the work

environment in the knowledge occupations related to their initial degrees. Concerns stemmed from

a variety of ethical, personal, and practical issues, and the actual nature of practice in their

occupations. It is difficult to assess whether different approaches to the initial decision-making,

such as in-depth investigations of the realities of practice in various occupations, and more

developed self-knowledge, would have resulted in different initial field or practice choices.

6.4.2 Theme: Choosing a change qualification. When most participants experienced

dissatisfaction in their initial occupations, they began to consider alternatives. Logically their

choice had to satisfy the reasons for their change decision, and be feasible. Finding an alternative

that met both these criteria was an essential prerequisite for enacting a change.

Sub-Theme: Reasons for particular new field selection. Flynn and Brian were unhappy

with the social isolation in their engineering work and both chose new broadfields which met their

social and interest needs. Flynn contrasted his initial and change broadfields. He was: “sort of

enjoying it (engineering), whereas political science and economics were really exciting”. After four

years of part-time study, he considered incorporating them in some way into a career, possibly still

in engineering. Brian found engineering isolating and impersonal, whereas “with medicine I feel

that you can and do have an impact on people.” He liked the hands-on parts of engineering, which

he thought might match with surgery. Similarly Kate had: “always wanted to be a Vet…the two

things I was always interested in were Film and Television and animals. I’d ridden horses since I

was about eight, and always had dogs.”

For several years Fay had worked away from human movements, in semi-skilled positions

in selling and in insurance. She found: “working in a call centre, I was still getting a lot of sickness,

and I think sometimes I’d get anxious about the work and about meeting my sales targets”. Fay

saw the job “as a stepping stone but I didn’t know where the stepping stone was leading”. She

wanted a profession that was “something that I could see as more of a career”. Teachers in her

family suggested teaching. She believed she was good with children and felt it would suit her.

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As a change from sameness of practice and remuneration in pharmacy, Melanie felt the

outcomes from accounting were many and varied. She was confident she’d have a choice of

practising alternatives and could mould a career if she was prepared to initially drop back in pay

and then work hard. Melanie was anticipating “there will be quite an increase in salary over time” .

The breadth of alternatives and the anticipation of ongoing challenges of accounting as compared to

pharmacy were the attractions, plus income incentives.

Cheryl’s goals in making her change seemed to be twofold. The choice of business

management “was really to get a broader degree behind my belt so I could move into something

different…something you could apply to different fields”. Though she liked podiatry itself, she

initially found the ongoing professional relationships with clients very demanding. Her enrolment

in business management and later health management because of her interest in health was partly

“to move away from working with people”. Additionally, because of the harassment problems she

had experienced, she hoped that “If I move away from a small business environment, then all of the

issues would be gone.” Similarly Lettice chose financial nanagement as a move away from some

harassment but more particularly from the intensive emotional nature of her law practice

experience.

Sue’s situation and solution were somewhat different. From her disillusionment with

facilities in clinical psychology practice as described, she had worked more in her alternative

speciality of organisational psychology for many years. She found herself transiting to mediation,

which required both clinical psychology and legal skills but tended to be dominated by the legal

profession. She felt she needed some legal expertise for appearance as well as use.

In summary, in choosing a change of field, participants sought alternatives that they felt

would allow them to move away from the dissatisfactions they had experienced following their

initial choice. Their change choices offered the potential for security of full-time employment for

the long term, or increased practice options, or a career structure, or work that was meaningful for

them as individuals, or a combination of these qualities.

Sub-Theme: Considering course admission requirements. Participants needed to choose a

course they could gain admission to, and a mode of study they could realistically afford. They

considered factors such as the length of the course, whether it was offered full-time, part-time, or

externally, and the extent of life style adjustment that would be necessary. The dilemma most

participants confronted was a choice between the two scenarios of studying full-time or studying

part-time. Studying full-time meant loss of income/lifestyle, followed by the likelihood of

restarting in a new field as a novice on a lower salary. On the other hand, studying part-time and

working full-time would be stressful, take longer, and would mean completion at an older age,

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which may have made competition with younger new graduates more challenging. Their

deliberations regarding course selection and experiences are described below.

All participants had completed their matriculation and initial tertiary qualifications at

universities in Australia. Prerequisite requirements for change course entry depended on the

particular course and the university. Having to spend time and money gaining the prerequisites can

be problematic for older students and a disincentive. As a result, programs without particular

prerequisites may be more attractive.

Only three participants had to meet specific knowledge prerequisites to enter their change

qualifications. The Bachelor of Veterinary science at the university Kate was considering had

science prerequisites, and could be difficult to gain admission to because of the very high tertiary

entrance rank (TER). Kate was able to satisfy the prerequisites from her year 12 studies in science.

Her TER after year 12 would not have been high enough but her excellent results in her Bachelor of

Arts Degree gave her a sufficiently high rank.

For graduate entry to the medical course Brian was interested in, a bachelor degree in any

field and a pass in an entrance exam were mandatory. Brian found it was “a fair bit of work… You

need to have first year university level in Biology and Chemistry. That was the hardest bit of

studying. I had to build up to doing (the exam).” Unfortunately it is only held once a year and he

had to sit twice: “I started Medicine in 2012, so I did the (exam) in 2010 and 2011. I began study

for it in December 2009.”

At the time Keith applied for Medicine in 1999 the course had just been changed to a four-

year postgraduate programme. To be accepted he too had to pass an entrance exam and had to have

completed a previous qualification no more than 10 years previously. After his Diploma of

Education, Keith had completed a non-award masters qualifying programme (one year part-time)

with a view to an eventual PhD, probably in history. He considered it serendipity that by the time

he completed the masters qualifying year, the university policy had changed and the masters

qualifying year had been converted to an award course. He was awarded the Graduate Diploma of

Arts which meant that he had completed a qualification within the 10-year requirement. Otherwise,

he would have had to complete another qualification before being eligible for entry to medicine, and

may not have considered the change idea viable. He was confident about the entrance exam and

passed on his first attempt.

None of the other participants had to meet specific extra requirements for their change

qualifications, though Lettice, like Kate, had to compete through the general undergraduate

admission system. She easily met the required TER from her previous tertiary studies in law. For

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mature-age re-entrants in changed broadfields, difficulty meeting entry rules can be an issue as it

nearly was for Keith.

Sub-Theme: Course length and mode of study. See Table 6.3. As part of their change

decision, participants had to consider whether course offerings in their preferred change field were

viable for their circumstances. Because of various problems in the employment situations in their

original fields, Fay, Kate, Cheryl and Melanie felt an urgency to commence study. Even though she

had full-time employment, Melanie wanted to complete her change qualification as fast as possible,

which strongly influenced her change choice.

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Table 6.3

Course Length and Mode of Study for change Qualification

Participant FTE of change

qualification Full/Part Time

Mode of

Study

Choice of

attendance/mode

Cheryl .5 years/1.5years Part time External Yes/yes

Melanie 1.5 years Part time External Yes/yes

Sue 2.5 years Part time Internal Yes/no

Flynn 4years Part time/

Full time Internal Yes/not sure

Lyn 3 years Full time Internal Yes/Not sure

Fay 1year Full time Internal No/No

Kate 5 years Full time Internal No/No

Kieran 4 Years Full time Internal No/No

Brian 4 year Full time Internal No/No

She described in her reasons for selecting a Master of Professional Accounting (MPA): “To do

accounting in undergraduate courses, you dabble in lots of other things like marketing, HR (Human

Resources) etc. whereas postgraduate is more directed. It is…accelerated… really very

specific…you can do a similar degree in half the time.” At interview she had experienced both

undergraduate and postgraduate study in the broadfield of business, and could see the difference.

She also stressed the flexibility in course structure as an attraction: “I enrolled in the (graduate)

diploma to start off. You just keep enrolling in units, so my plan is to finish the masters as it is only

an extra four subjects, so an extra six and a bit months.” There were also some negatives. She saw

such postgraduate courses as much more intense, making the study/ full-time work combination

very demanding. She tried to both study and work full-time, but had to reduce both to three

quarters of a full-time load, which was still very demanding. She had chosen to do the course

externally which allowed more time to study and work.

Similarly, Fay chose her Graduate Diploma of Education (Primary) because it took only one

year full-time. She also described the course as “fast tracked” and so very intense, especially during

practical placements where lengthy lesson preparation was required each night. As a single person

without family responsibilities, she was able to cope by moving back home, but described a high

dropout rate among her fellow students with families. Cheryl was also single when she commenced

her business qualification, but chose to maintain her independence. Like Melanie, she had opted for

a part-time, external professional masters degree while working full-time, but did not mention the

intensity of the course:

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If I studied by correspondence I could still work and I don’t generally watch TV or lie

around … I am constantly doing things. I think the study thing works for me. It’s a good

distraction and it kept my brain working.

Sue too selected the postgraduate option for her mature-age study of law:

The Juris Doctor appealed on a number of grounds…it was for postgraduates, run on a

seminar basis …small seminar groups of, say 20. It was however a lot more expensive but

...it would be recognised overseas and would give me flexibility.

It was also offered part-time which suited Sue’s family circumstances. She would have liked to

have studied at a university that offered a combined Law/ Modern Asian Studies Degree, but it was

only available full-time. Five years full-time on campus was out of the question in her situation, so

that her life-stage responsibilities limited her choice.

As Medicine and Veterinary science qualifications are only offered full-time on campus,

Keith, Brian, and Kate had no options. As discussed, the changed structure and length of the

medical degree strongly influenced Keith’s change decision:

It (the desire to do medicine) had always been there but I had been studying for 3 years (to

complete his BA) and Medicine would be another seven (including the first practice year) so

that was 10 years, and I thought I would have no money or a house and all that sort of thing.

Therefore he had instead completed the diploma of education which was only one year after a BA.

However, after he had taught for nearly 10 years, fortuitously the medical course was shortened to

four years, and he felt it was doable.

Lettice had planned to major in both maths and business with a view to actuarial studies,

but later accepted advice that actuarial studies would be too difficult and lengthy at her age, so the

length of training time had impinged on her decision-making. Flynn also chose another

undergraduate degree for what would become his change qualification, but the part-time study was

initially for pleasure, so time to completion was not an issue. He still felt it was “hard going.”

In summary, participants considered several practical issues in assessing the feasibility of

making their change. In particular they had to assess ease of entry to the desired course and the

attendance options in view of their lifestyle and financial situation, and possible employment and

remuneration outcomes.

6.5 Chapter Summary

Most participants sought the opinions of family and friends when deciding on the

broadfield of their initial qualification, and in many cases of their change qualification; there was

little in-depth exploration of options, of the nature of likely occupational outcomes, or actual

degree-related employment opportunities. Participants’ motivations for returning to study a change

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qualification generally stemmed from what several called dissatisfaction with their initial course, or

the employment outcomes from that course, or in one case, add-skilling. In making the change

decision they had to weigh these motivations against the practical issues to be overcome in the

change process. Chapter 7 will explore how participants’ decisions were impacted by their age and

other contextual issues, and will consider the outcomes of the change of broadfield.

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Chapter 7

Change Processes

Chapter 6 presented the two themes, Decision-Making and Returning to Study, which

addressed Research Question 4 on why people changed broadfield. This chapter addresses

Research Question 5, concerning the change process and focussing on demographic and contextual

factors influencing change decisions and outcomes. The findings in relation to the identified main

themes of ‘Individual-in-Situation and ‘change-outcomes’, focussing on participants’ reviews of

their change decisions and the outcomes of those decisions, will be illustrated with participants’

quotations.

7.1 Main Theme: Individual-in-Situation

This Main theme incorporated the themes Age, Financial Issues, and Gender. Together,

these three themes encompassed the relationship between age and personal and financial aspects of

the process of undertaking a change of career direction.

7.1.1 Theme: Age. Several issues relating to participants’ age arose during interviews.

Findings related to the influences of maturity and life-stage issues, and the attitudes of employers in

the world of work to age factors, are discussed next.

Sub-Theme: Maturity issues. Many participants mentioned the influence of their level of

maturity on their approach to initial decision-making, and eventual degree selection. Maturity was

also seen as relevant to experiences and performance at university, and to readiness to cope with

professional relationships.

Maturity issues affecting initial degree selection. Keith and Kate had been interested in

medicine and veterinary science respectively since early high school, but both acknowledged that

immaturity diverted them from initially selecting those fields. Veterinary science and, at that time,

medicine required high tertiary entrance scores. Keith had not been prepared to study hard at

school.

Through high school …I basically mucked around… played lots of golf and went out a lot

and things like that. I’d be on the golf course during swatvac (time off before exams to

study)… I know I was perhaps too hedonistic. From about 15 to 25 it was about how much

I could enjoy myself and what I could do.

Kate had a similar attitude to study:

Come grade 11, I thought right, I’m going to study hard and get an OP1 and be a Vet. Then

about a month into Grade 11, I realised that that was a lot of work (getting an OP1) and

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maybe I didn’t want to be a Vet that badly, and so I abandoned that plan, basically because I

didn’t like school work very much.

Kate and Keith acknowledged their avoidance of the commitment to study and the responsibility

involved in the practice of their careers of interest.

Melanie considered that ‘immaturity’ contributed to her decision to drop out of radiography

and thus, for her, medicine, after one bad practical experience:

I think that when you are only 17 or 18 you think about things in a slightly different way. If

I had have been in that situation now, I probably would have tried a bit more than just one

practical, but I think at the time I felt that I didn’t want to waste my time doing another year

if this isn’t what I want to do.

Immaturity was thus seen by different participants as affecting their decision-making processes

during school, and initial university studies. The next section discusses the perceived effect of

immaturity in the work force.

Maturity issues in professional practice: client-professional relationships. The importance

of personal maturity and life experience in professional practice, particularly with clients in distress,

was stressed by Lettice, Fay, and Cheryl, who had not considered this aspect in their initial

decision-making. They felt their struggles to cope professionally with the emotional demands of

degree-related work in their fields resulted partly from their immaturity. The result for Lettice was

emotional trauma which left her little choice but to return to study in a different field:

Coming straight out of school, straight into University, straight out into the working thing (I

was 22 when I started working) as a solicitor doing Family Law when I hadn’t had any

experience of life myself. I was single…and handling fairly difficult complex family and

marital disputes, custody battles, allegations of abuse…. I think it was just taking on all

these family cases and I just wasn’t emotionally mature enough for it.

Fay had a similar though less intense experience in her final practical in human movements.

She was also 22.

I’d never dealt with any big adversity or problems so that I found it difficult to relate with

these people that I was trying to help. A lot of these clients were angry people and they

didn’t want to be there. I think I was too young for it. I was very young, and so far

everything had gone along fine…difficult to explain! I think they saw I wasn’t experienced

enough to be able to handle the work.

Cheryl also felt unprepared to handle the relationships that clients expected: “I think that

when I first started working, it was all a bit too much for me. I was a little bit immature and

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perhaps I wasn’t as caring at that point.” Rather than working for one Podiatry practice, Cheryl

initially tried working as a contractor in several practices, but, she said “I think I was a little bit too

young and not experienced enough to be handling the business side of it well.”

In summary, several participants felt that on initial graduation they were not emotionally

mature enough to establish good rapport with clients and cope well with the stressors professional

practice. As discussed in Chapter 6, these participants also expressed disappointment that their

degrees had not prepared them for the relationship aspect of their fields.

The effect of maturity issues on participants’ university experience. There was a consensus

among participants that when they returned to university in their late twenties and thirties, they were

better students than they had been during their initial degree. Keith explained: “It (medical degree)

was the only one where I did a little bit of application and any decent work.” In his earlier

postgraduate Diploma of Education Keith “got into a bit of trouble at times in the classes. I was just

not paying attention and was not really interested so I resorted to making silly jokes etc.” Kate was

very explicit: “That’s what I said and I say loudly now, that if I gotten into vet straight after high

school, I would have dropped out. At 17, I wasn’t mature or focussed enough to do it.”

Brian and Melanie talked of the conflict in their initial degree between studying and having

fun. They contrasted that with their serious application to study in their second qualification as

Brian described: “When younger you want to go out more, …do more…whereas now I’m more

settled I guess, and able to study all weekend without feeling like it’s a big deal.” In her first degree

Melanie “used to skip lectures quite a bit… after so many years of studying all the time at school”.

From her experience: “You go into uni,… have the freedom and you’re not set into a schedule” .

Her attitude seemed very different for her change qualification. “For financial reasons, I started out

still doing my 40 hours (in a pharmacy position) and enrolled full-time.

Similarly Lettice felt, “in my first degree I just got the basics of the law, studied what I had

to for the exams…. I wanted to go out and party.” She saw this as being in sharp contrast to her

attitude in her change degree. “This is my second chop at it, and I need to get the best results I

can…. I was working and had commitments so I had to put the study in and turn up to tutes etc.”

Flynn made a particular point about his different attitude to study. “The first was like school but

this second one was to learn… I found that I used my time well to study and read around, which

was a completely different approach to when I did my first degree.”

There seemed to be general agreement that, despite additional responsibilities, participants

felt they were more dedicated to their study and more interested to learn when they undertook their

change qualification than they had been during their initial degree.

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Several participants felt they had lost some confidence in their ability to study successfully

when they began their change course. Fay explained: “I’d had a gap from studying and I didn’t

know how well I’d go back to studying again or whether I could remember how to write

assignments.” She also mentioned a fellow mature-age student who “hadn’t been back to study for

eight or nine years, so she was worried about how she would go”. However, Fay recalled

techniques she had previously observed mature-aged students using:

Well I got to use the tutors more. I could see that back in my undergraduate time the mature

age students would get a lot of help from the tutors and lecturers, so I thought that would be

the way to go.

Similarly, when contemplating her maths degree, Lettice felt “Law really changes the way

you think as well, I was concerned re the maths”. As she saw it, “ a lot of these people (kids) doing

the degree would be straight out of school having just done maths A, B or C”, so she “did a bit of

summer schooling over some of that 3-month break”. Even then as she recalled: “I did the calculus

exam which I had studied so hard for and got in there, looked at the paper and just went blank.”

This surprised her as she had enjoyed maths at school and done very well, but she decided to drop

the subject. Lettice placed herself under added pressure because “I was also not straight out of

school and wanted to achieve the best possible results.”

Thus, some participants were concerned about their ability to achieve academically when

older, and put pressure on themselves to get excellent results to be competitive in the world of work

Others relied on techniques they had learned from mature-age students with whom they had studied

in their initial degree.

In addition to application and confidence, several participants commented on the social

experience of returning to study. Kate mentioned how she felt at the beginning of her change

degree: “Going in for the first six months to a year I felt like an imposter.” Later she found: “They

(Vet School) really do create a feeling of being part of a community. Everyone parties together and

hangs out together and then come exam times, everyone breaks down together and stresses.” She

appreciated the support as it was “a very demanding degree and very stressful”.

In Fay’s postgraduate diploma, the ratio of young to mature students was “probably about

half and half”. For her the experience “was different because I got to walk in as one of those people

who had already done it.” dFay found it easy to make friends: “Having my previous work

experience, I was really confident talking to strangers and people.” She was also confident enough

to use the tutors more.

Before Brett started his medical degree he had correctly expected more older students. He

was finding “there are more people that I’d interact with, that I might have as friends… If they were

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17 and I was 27 it would be too much of an age difference to actually be friends with people.”

Cheryl and Melanie studied externally and did not mention their student experience on returning to

study. Flynn and Keith also did not discuss student interactions on return to university, but stressed

their enjoyment of the course content.

Summary. The issue of maturity was mentioned by most participants. Immaturity was seen

as negatively affecting initial decision-making and initial success in professional employment, often

bearing on the decision to return to study. Conversely most participants mentioned the positive

effects of maturity on their approach to study in their change qualification. Most were happy with

their experiences as mature-age students, but were also conscious of the influence of life-stage

issues on many aspects of their quest for occupational change.

Sub-Theme: Life-stage issues. The term ‘life-stage issues’ in the context of these findings

incorporated issues which arose for participants as part of life progression both personally, socially

and financially. In several ways these issues influenced their decision to return to study per se, the

study mode choices made, and outcome options.

Life-stage issues included things such as settling independently, finding a partner, rearing a

family and achieving financial security. The situations mentioned by participants were not only

those which were considered at the time the change decision was made, but also aspects that would

continue to influence outcome decisions in an ongoing way.

The consciousness of life-stage tasks was reflected in Fay’s explanation for choosing a short

1-year (over a 4-year) full degree course.

It would have been too long a time for me to give my life up, I think, when I was (already)

27 or 28. I’d been in the work force too long to have to go out of the work force for four

years, and part-time (study) would have taken so long. The one year was really the clincher.

I know you’re not really stopping it (life) but you can’t do anything else in that time. You

can’t gain money; you’re going to be older at the end of it.

Implicit here is that she saw studying as belonging to an earlier life stage. She had moved on to

other tasks which included gaining financial security. She saw that these would have to be put on

hold, but preferred not to have to do so for too long.

Independence: being in a partnership. On completing their initial degree, most participants

looked to move from dependence on parents to independent living, possibly within a relationship, or

to marriage and a family, and mentioned the effects of these life-stage developments on change

decisions. Several participants were in relationships prior to commencing their change

qualifications or had moved into these relationships before completion, and noted several

consequences.

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Melanie and her partner had purchased a property in her first semester of change study.

“We bought the house during that period which was extremely stressful with my partner away,

trying to send documents away to him etc. It was a nightmare…I got behind.” With the shared

mortgage, she felt she had to work full-time while studying, whereas she believed, “If I was by

myself I wouldn’t have bought a house. Not yet anyway.”

Both Lettice and Cheryl met their partners during their change study. Both partners had

positions which would mean staying within the State. As Lettice explained:

My partner and I were starting to get a little more serious which meant I was limited, by the

end of my degree, as to where I could apply. A lot of the fund management degree jobs

tended to be interstate, and that was out of the question for me.

Thus she felt she had to do well at university to compete with younger graduates in a more limited

job market. Similarly, Cheryl said: “I suppose I am on hold …until he (partner) finds out where he

is going.” Conversely for Melanie, “My partner is an electrician so he is pretty flexible.”

Family commitments. Several participants mentioned having a family as also limiting

flexibility and thus employment and career development options from change decisions. Both

Flynn and Lettice had completed their change degrees and found employment in their early thirties

before their children were born. Keith married at age 34 during his medical course.

I was just newly married and I think my wife was pregnant …during my intern year. A

baby was due early the following year so I thought I didn’t want to go away for a month or

something with a new baby.

He completed his intern year and looked to specialise but faced a problem. “My wife was going to

go back to work and we wondered who would look after the baby.” He tried to combine parenting

with specialisation so his wife could work, but found the combination impractical. As a result he

surrendered the opportunity on offer in ophthalmology and settled for general practice.

Sue was also trying to combine her parenting role with full-time work and part-time study.

She was the breadwinner for the family. She recalled: “… everyone found it very stressful. Well

they did.” With late teenage children, she felt family pressure to maintain, for example, her

assistance with their school and university studies. Sue was conscious of their anger at her “busy

state”. Her husband disagreed with her returning to study: “I think he thought I was busy enough…

didn’t spend enough time with the family, or something”.

Several participants mentioned the stress and restrictions of returning to study with family

responsibilities, and the limiting effect being in a partnership can have on career development

outcome options.

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Sub-Theme: Age and the world of work. Participants’ perceptions about the effect of their

age on finding degree-related employment after their initial qualification varied according to the

particular broadfield they were trying to enter. Fay was the only one who felt her youth negatively

affected her employability (as a personal trainer) at that stage.

In the fields of business and of law, youth was perceived to be an aid to employability so

that in their second study, as mature-age students, some participants pressured themselves to excel

academically to compete successfully for positions. Both Melanie and Lettice raised this concern.

Lettice “wanted to achieve the best possible results”. She “thought they (employers) would be

looking for younger graduates. They might have no attachments or ties and things like that and

were free to move around”. As a result she pressured herself: “I thought I had to get the best results

so that I could have something to compete with.”

Flynn felt particularly lucky to have graduated in economics and political science when the

Commonwealth Treasury “had decided to pick up a few more people during the recession of the

early 90’s…. so they weren’t just picking up young people, which are what I tend to see in other

intakes”. He saw this as “a little outside the norm”. Flynn had observed that most new graduates

in the Public Service tended to be young people who had just completed their initial degree.

Sue discussed factors against the employment of mature-age new graduates in the legal

profession:

I wouldn’t have gone into one of the big firms. The organisational climate is not conducive,

and I’m too old… If I’d gone into one of the larger ones, they would have had to have had

the imagination to see how they could use my skills.

She felt a firm would have to weigh up the benefit of her experience against the higher GPAs of

younger new graduates. “I would have had to have worked harder to boost it up. It was just below

what was needed.” She found this attitude of law firms frustrating:

I mean I would have been arrogant enough to argue that if I could get the GPA I had while

running a counselling practice and a consulting business, that I’d hold my own against a new

graduate who happened to get a few points higher in GPA, but hadn’t worked and was

supported by parents.

In summary, attitudes to employing mature-age new graduates were perceived to vary

according to broadfield of study. Several participants had been concerned that young graduates are

preferred to more mature graduates in the business and law fields, and thus felt pressure to strive for

excellent results in their change degrees.

Advantages of mature age in the world of work. There was also acknowledgement that age

brought advantages, such as maturity itself and contacts made through life. As a mature business

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woman who had held high level positions, Sue had contacts: “I suppose I knew at least three

partners of three major law firms that I could have probably approached, plus I knew other lawyers

through (a Service Club).” Unfortunately they were not in the areas of family and employment law

where she wanted to practice.

The benefit of being older in professional practice was stressed by educators during Fay’s

Diploma of Education Studies. Maturity was presented as countering the prejudice in some of the

profession to primary teachers who were only one-year qualified. Many in the one-year course

were mature-age students returning to study. Kate had encountered an additional issue that mature-

age new graduates and employers may confront. She was currently working under the supervision

of a veterinarian who was several years younger than her. Though it was working reasonably well,

Kate was conscious enough of the situation to mention it as an issue she had to adjust to.

There was some acknowledgement of the advantages of being of mature-age, because of the

usefulness of experience and increased opportunity to have developed an array of contacts.

Summary of age. The theme Age incorporated the effects of age/maturity per se on

decision-making, academic ability to study at university, application to the study process and

coping with the emotional demands of professional practice. The disadvantages and advantage of

maturity in many of these areas were stressed by most participants. The concept of life stages and

associated tasks also featured in the theme, Age, in relation to conflicting demands. The pressure to

proceed through life-stage tasks was in some cases seen as competing with the desire to return to

study and some individuals felt the outcome of their change degree was limited by the related

constraints. All participants stressed the importance of financial issues in any decision to undertake

a change qualification with a view to changing occupation.

7.1.2 Theme: Financial issues. All participants stressed the centrality of financial issues in

decision-making around course options, in tandem with life-stage tasks. They had all lived at home,

supported by parents for their initial degree, with part-time student jobs. Several had delayed their

student loan (that is, the Higher education Contribution Scheme; HECS) payments. Participants

alluded to three main categories of financial concerns associated with returning to study: paying for

the course itself; funding living while studying; and lowered income in a new-graduate salary on

completion.

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Table 7.1

Methods of Payment of Course Fees and Remaining Debt for all Participants

Participant Payment for

initial degree

Payment for change

degree

Remaining debt at

interview

Age at

interview

Sue No Fees Australian Full fees

(Payment delayed) Yes 61

Flynn No Fees No Fees No Debt incurred 49

Kieran No Fees HECS (Delayed) No 46

Kate HECS upfront

(parents) HECS (Delayed) Yes 30

Lettice HECS up front HECS (Delayed) Yes 41

Fay HECS Delayed HECS (Delayed) Yes (including

some initial debt) 30

Cheryl HECS Delayed HECS (Delayed) Yes (including


Melanie HECS Delayed HECS (Delayed) Yes (including


Brian HECS Up Front HECS (Delayed) Yes 27

Sub-Theme: Paying for the courses. All current participants had completed their initial

qualifications full-time and so were not eligible to earn full-time professional salaries until

graduation in their early twenties. At interview, two were still paying off student loans from their

initial degree (see Table 7.1).

Sue completed her earlier qualifications in a period before there were loan schemes. Her

parents funded the first two years tuition until she qualified for a Government scholarship, which

paid the fees with no repayment required. Flynn completed both his initial and change

qualifications when fees for university study had been abolished (temporarily). Similarly there

were no fees for Keith’s initial qualifications. However, both Keith and Sue had to pay for their

change qualifications which were both completed after university tuition fees were reintroduced.

As mentioned above, some participants weighed up the cost/time issue when selecting a

change qualification. A shorter masters course was, according to Melanie: “a lot more expensive

actually. I think it is about double the cost of undergraduate courses, possible around $10,000 a

year.” Sue’s change degree, Juris Doctor, was also a high-fee course, but she was able to obtain a

student loan which she was still paying off. Shorter courses could mean earlier entry to the change

occupation, which would be important for people who were not able to find adequate employment

from their initial qualification.

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Everyone except Flynn funded their change qualification via a government loan scheme,

mostly HECS, and acquired a debt. This was a source of unease for the three with an existing

HECS debt. Fay and Melanie had not been able earn a full-time professional salary after their

initial graduation, and Cheryl had returned to study after only a short time in practice. They had not

been able to reduce their initial debts very much. Still, Cheryl, whose total debt after her change

degree was “huge”, thought of it as “part of my tax”. However, though she would like to do more

study out of interest, she said, “probably not, because of my HECS debt…I would pay upfront next

time to get ahead…I’m better off to wait until I’m ready to do that.” Fay, Cheryl and Melanie had

accumulated HECS debts but only Melanie seemed concerned. At interview, Keith had had nine

years of medical practice and was the only one with no remaining course fee debt.

In summary, the cost of obtaining an additional qualification at least at bachelor level was a

major issue for participants considering a change qualification, especially for those who had a

continuing debt from their initial or other qualifications.

Sub-Theme: Funding living while studying change qualification. Supporting themselves

during change study was highlighted by all participants as a main concern, and their solutions

differed according to their social circumstances. Four people without family or relationship

commitments stayed at home or returned to live with parents, two of those whose change courses

did not offer a part-time option had partners who were working, and three opted for part-time study,

in external mode for two of them. Most were able to obtain part-time work related to their past

qualification

Living with parents. As mentioned, four participants who were single either continued to

live at home with parents or returned home specifically to reduce living costs. The importance of

being able to live at home was summarised by Fay: “I had to find a way to support myself…I

thought if I moved home that would take the pressure off paying rent… My parents were agreeable.

They said they’d help me.” Fay discussed rationalising her finances: “I’d saved a bit of money

working (at an insurance company) so I paid off my car loan. I think that was my only really big

debt. I still had to pay my mobile phone bill”. She was able to claim a student allowance from the

Government (Austudy) which helped her stay out of debt. She also compared her situation to that

of friends’.

I was lucky as I was in a situation where I was able to go back and do uni whereas I couldn’t

see any of my other friends being able to. They already had a house, some were starting a

family, or didn’t have enough money, or they had a situation such as parents they couldn’t

stay with.

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Her opinion was reinforced by Kate and Lettice. Kate explained: “They (parents) were very

supportive so I was lucky in that way that they were willing to …well…put up with me.” She

mentioned friends in the course: “...there were plenty of people who were that sort of age, had

children or other commitments and a lot of them struggled more than I did.” Lettice echoed this

comment: “I guess it’s not like someone who has already established themselves and their family

with that extra burden, wondering how you would manage. I had my family (parents) that if I really

was struggling, my parents were there anyway.”

Flynn was still living with his parents when he returned to university full-time to complete

his change qualification. He summarised: “So what allowed me to move to a different career was

having done some of the study part time while I was working, the redundancy payment, not having

onerous course fees, and having the support at home.” Living with parents was seen as allowing

these four participants to do their change qualification full-time on campus.

The part-time/external study alternative. In contrast, Cheryl, Melanie and Sue who had

families and/or mortgages, were in relationships, or who valued their independence chose part-time

study and continued working as much as possible. As Cheryl explained: “I had had an income

even though it (working in podiatry) wasn’t what I wanted, and wasn’t willing to give that up and

move back home”. Sue was supporting her husband and two children and adapted her work: “I

figured out I could do counselling…there was this medical practice up the road that had no

Psychologist”. She started a private practice there. “So when I was doing the Juris Doctor course I

was doing consulting work like mediations, reviews, and then I was doing the counselling too.”

When full-time study was the only option. Part-time study was not an option for Keith or

Brian. Brian described his financial situation: “It’s fairly tight. I get the Centrelink allowance,

Austudy, and I live with my partner. She’s working full time.” However he was also doing some

casual work and believed he would cope if single: “I think it would be ok… At the moment we still

split bills and things. With the Austudy I’d manage. If I didn’t have that I’d be living at home.”

Keith asserted: “Financial was a big one (issue) when thinking about what I’d go back and

do”. Keith had an investment property with his brother which was reducing his ability to fund a

return to study. When his brother wanted to sell it, Keith thought: “…that would free me up too, so

we sold the house and I thought there was nothing in the way then to doing medicine and that I

would apply”. He married in the second year of his medical course and his wife worked full-time.

Having some expertise from a previous qualification was advantageous for Fay and Lettice

when they undertook full-time study. They were able to obtain casual work related to their previous

degrees. Fay continued coaching in two sports and Lettice obtained tutoring and worked at legal

aid. Some of Brian’s casual work was related to his current medical enrolment. Kate was fortunate

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to be able to do receptionist work for her father in the holidays. Thus most participants found their

existing expertise could be utilised to earn some money while studying, without trying to compete

for the usual student jobs.

Sub-Theme: New graduate salaries. Several participants were concerned about a drop in

salary on entering their change occupations. (Borghans & Golsteyn, 2007; Dolton & Kidd, 1998;

Pavan, 2011). Apprehension about the level of new graduate salary related to the age or life stage of

the participant, the reason for the change, and the particular broadfields involved.

This was a dilemma for Sue and others in her law course: “I was making good money… I

was the breadwinner. To become a Judge’s Associate would have been a substantial drop in

money”. If she had gone into a large firm: “Until I got to be a senior, I’d have been on less pay, and

for what, at my age”. “A number of the Juris Doctors (students) were actually earning more money

than the lecturers and senior lecturers”. Melanie anticipated “quite a drop in salary to start

with…but then I hope maybe in five years I will have made up the ground plus more”. Her situation

differed from that of Sue; she was much younger, sharing her mortgage, and did not have a family

to support.

On the other hand, for Kate and Fay, any professional full-time salary would have been

welcome and neither expressed concern about initial salary levels as a consideration, though Fay

had worked for two years full-time in the insurance industry. Kate too was very focussed on

degree-related employment and did not mention any pre-enrolment concerns about salary.

Keith and Brian did not appear concerned about initial salaries in medicine. Brian felt the

beginning money “is in the upper end of what graduates normally get, and then it goes up from

there”. Keith did mention financial difficulties general practitioners encountered opting to

specialise later: “It would be 5 years as a registrar and financially it wouldn’t be possible. It would

be a nightmare”.

Returning to new graduates’ salary levels was an issue, particularly for those with financial

and family commitments. In two cases this issue obstructed participants’ professional development.

Been a s

Summary. Financing living expenses while returning to study was a major issue for all

participants. Part-time and external study options eased the process for some, and continuing to live

at home, or returning to home, enabled full-time study for others. Most of those living with

partners felt they wanted to contribute to expenses. Several participants mentioned that the skills

and knowledge from their initial degree made it easier to get reasonable part-time work during their

change degree, reducing the need to compete for the usual student jobs with younger students.

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7.1.3 Theme: Gender. The influence of gender on the change process was occasionally

raised directly and there were other subtle references. Sue was very angry in high school when her

GP said that specialisation in medicine was not suitable for females, so she rejected medicine as an

option. She also pointed out that if she took the full-time position offered by the Navy, she would

never be allowed to go to sea (> 40 years ago) because of her gender. In describing difficulties her

fellow students experienced coping with the work load in the graduate diploma, Fay specifically

mentioned that several mothers had to drop out because their parenting responsibilities meant they

were unable to complete assignments. “Some of the girls in my course …were mothers with two,

three or four kids. They’d come to uni and say, ‘I couldn’t do it. I haven’t done it…the kids had to

be dropped…I had to make dinner’.”

Attitudinally, both Lettice and Cheryl seemed to accept that their partners’ positions took

precedence over theirs and were prepared to undertake whatever ongoing relocations were

necessary. However, Keith’s decision to look after their baby, and thus surrender the option of

specialising so that his wife could return to work, suggests a different attitude to gender roles.

Thus gender, traditional gender roles, and attitudes to gender were seen to affect field

choices of females, and limit their freedom to carry out further study or to control change-outcomes.

7.1.4 Summary of main theme: individual-in-situation. Age, associated levels of maturity

and gender were seen as variously influencing initial degree choices, employment outcomes, and

coping with professional roles, often bearing on the decision to return to study. Life-stage issues,

role demands, and commitments seeded serious financial concerns not only about funding the

change course and living expenses, but also about paying off previous student loans, and managing

on an initially reduced salary in the new occupation. Such concerns often dictated the attendance

and mode of study selected, though many participants were able to obtain casual employment using

the expertise from their initial field. Factors governing the outcomes of the change process are

discussed under the fourth main theme: Outcomes of the change.

7.2 Main theme: Outcomes of the Change

The various issues encountered by participants in the process of returning to study in a new

broadfield have been discussed. No participant had experienced failure in courses undertaken,

though Melanie and Brian were only part-way through their change qualification. The fourth Main

Theme considers employment following the change qualification, the perceived skills-and-

knowledge-based nature of the change, and satisfaction with outcome.

7.2.1 Theme: Employment following change qualification. The participants’

employment outcomes stories are presented here. Kate, Flynn, Lettice, Keith and Fay all eventually

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found full-time positions in their change occupations, though Fay’s employment was a series of

contracts at the time of interview.

Though not their initial intention, both Cheryl and Lettice found their

business/Management/Finance studies combined well with their initial fields of work and

employers encouraged the combination. For Lettice, the Government position gave her the

opportunity to combine her newly acquired business and finance knowledge with expertise from her

initial degree in law. As a 30-year old new graduate she was also offered a graduate position in a

bank. Similarly, Cheryl moved to a position in a different podiatry practice when she had almost

completed her change qualification and “everything kind of fell into place”. She was given some

management duties so that she combined podiatry clinical practice with health administration as the

only person in the practice with appropriate expertise. Both were very happy they had completed

their change qualifications.

Kate and Keith moved into health professions with multiple pathways on offer, and both

considered specialisation. Kate chose equine science which required “3 or 4 years away, possibly

overseas, earning very low pay for working seven days per week”. She felt “it would not have been

possible if you had a family to support”. However after a 2-year internship, the equine expertise

enabled her to get a job as an all-round country veterinarian two hours from her parents’ home. At

interview unfortunately she was dissatisfied with the income level and was struggling financially

with the cost of living and her large HECS debt. In the small town where she worked, she was not

able to find someone to share house costs.

Married and with a new baby, Keith tried the path to specialisation in medicine but found it

compromised his time with his family, and settled for general practice. He concluded that

specialisation at that life stage was incompatible with maintaining marriage and effective parenting.

He had not quite given up on ophthalmology, but felt the required long hours of hospital work, the

huge financial cost of training and the serious reductions in salary during training were out of the

question. He regretted that he had not begun his medical degree earlier in life.

Both Flynn and Fay felt their post-change employment resulted from serendipitous

occurrences. For Flynn it was the unusual decision of the Commonwealth Treasury to recruit older

people with mixed degrees because of a shortage of work generally when he graduated. Fay,

despite her many volunteer hours, doubted she would have found it quite so easy to get work in the

over-saturated new graduate primary teacher market without the recommendation of her relative in

the teaching profession. At the time of interview she had been at a particular school for two years

and was hoping for a longer contract.

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At interview, Sue had not been admitted as a lawyer and was continuing with her

counselling psychology practice and some HR consulting work. She had not followed up her many

contacts in large law firms or explored options in employment law. Other issues dissuaded her

from seeking employment with a large law firm. “I had flexibility in my own business …it was the

flexibility, the money and I suppose the hours”. After a while she felt her friends were pressuring

her to do something with her Law degree:

So when I was under all of this pressure… I was thinking it’s hard to crack into the

mediation because the lawyers have got it. A lot of them are doing family dispute resolution

….I could have taken some courses initially but…..I thought that I did not want to do this

work (the extra courses).

She realised later this was “a stupid move in terms of the closing of avenues… I didn’t do the

initial family dispute resolution training, and then they made the subsequent training more

rigorous.” Now there is a training and registration requirement. Sue was considering her options

including an entry into family law practice via an alternative practice model. She has done a short

course to join the relevant association. While juggling all these ideas and options, she has

combined her knowledge and skills and compromised by offering HR advice and employee

assistance to Law firms and their clients. To aid this, Sue has developed a website.

In summary, the outcome options for each participant appeared to be governed by an

interaction between the particular broadfield changed into, the age at which the change was made,

and their social situation and life-stage. The next section considers participants’ evaluations of

whether or not their change was, in practice actually disjunctive.

7.2.2 Theme: Perceived skills-and-knowledge-based nature of change. At the outset,

most participants had considered their change would be disjunctive, and it was interesting to

compare that perception in relation to skills and knowledge sets with their comments on course

completion. Only Sue made her selection with the particular intent of adding skills to her existing

skills to attract more work in an area she was already equipped to practice in. “Well I thought that

probably, I would do more mediation, I suppose, because the lawyers were hogging that, and I

thought having a combination of psychology and law, I’d do it better”. In a somewhat more

tangential way, while Lettice wanted “something that was different to law”, she did indicate that “I

did not want to completely forget I’d spent five years studying law and three years practising it.”

All others saw the change they were planning as disjunctive, such that they were developing

new skills and knowledge to be used as a standalone set. When Fay changed from human

movements into primary teaching she did not consider there were any similarities between the two

fields in practice. For Kate, veterinary science “was such a big change”. She did not expect any of

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the skills and knowledge from the arts degree to transfer to the Bachelor of Veterinary Science.

Similarly, Flynn saw the change from engineering to political science and economics “as a fairly

big change”. “It wasn’t just a natural movement.” Brian also changed out of engineering and

commented that his engineering “knowledge doesn’t really translate except for some of the physics

a little bit such as knowing how x-rays work”. When Keith changed from teaching to medicine he

felt “there was no transfer of anything from my previous university study”.

Cheryl and Melanie both changed from health to business, though their narrow-fields of

study in both broadfields differed. Cheryl “looked at the Masters of Business Management as a

way to get a broader degree behind my belt so I could move into something different”. Melanie

described her move: “…it is a little bit like a real change. People do see it as quite different when I

tell them what I’m studying. As far as practical skills and knowledge go, there is not really overlap

at all.”

Thus the majority of participants viewed the change they were making as disjunctive at the

time the change decision was taken. However during their change qualification and as they

proceeded again into the world of work, there were some changes to these perceptions. Though it

may not have been considered initially, both Cheryl and Lettice found that their change had resulted

in a situation of add-skilling rather than change-skilling because of the particular positions they

obtained.

Fay discovered that her two broadfields “were quite related because within human

movements, I was teaching people how to exercise. In primary I was just teaching something

else…. letters and sounds and things like that.” This perceived transfer of training and knowledge

gave her confidence and she realised “I can do this. I’ve had experience with this.” Kate, to her

surprise, also found some transfer of training.

I guess some of the things I learnt in psychology that were more on the science side

definitely came in handy for things like neuro science. I really loved stats in psychology…

so when we did very simple stats in vet, I thought that was amazing and I loved it and was

way ahead of the curve on that.

These transferred skills were also applicable in employment. “The most useful thing I did

when working in America was do up a spreadsheet ... I did up the stats for my boss for the last few

years to show how everything had gone.” Kate stressed the importance of the generic skill of

communication that she developed in her initial course and in her work experience. “The running

of auditions was somehow comparable to consulting as a vet.” She felt she had the advantage over

younger people in the veterinary science degree. “There are a lot of courses in communication.

Most of the problems you have as a vet are communication failures, but I always did very well in

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those classes, because I had those skills going in.” She felt that 20/21 year olds “still looked like

children and struggled to talk, especially to grown-ups, about things on a serious level.”

Though acknowledging little carryover of his “numerate skills…physics knowledge…and

rigorous analysis skills” from engineering to economics and political science, Frank remarked on

the usefulness of his understanding of the scientific method. “I think it gives you a different

perspective on the knowledge claims made by other disciplines such as economics and political

science.” Brian contrasted engineering method with his change choice of medicine.

Engineering gives you thinking skills. It teaches you how to approach problems and how to

think about certain areas. Medicine is very much knowledge based. You have to go in and

learn a text book; learn every word in the textbook.

However he anticipated that in later years of the course he would “use that knowledge to begin

problem solving” so he may use those skills from engineering.

In summary, although most participants believed their change was disjunctive with respect

to core skills and knowledge, many thought that expertise gained in their initial qualification and

work experience (particularly regarding people and thinking skills) was transferable to employment

in their change broadfield. In the next section, participants’ evaluation of the outcome of their

change decision is presented.

7.2.3 Theme: Satisfaction with outcome. Most participants were happy with their decision

to undertake a change qualification, despite the various financial and lifestyle costs. Lettice and

Cheryl were happy to find employment with good future expectations where they utilised both their

areas of expertise. It had been unfortunate initial employment situations that had led to their change

decisions, rather than a dislike per se of the initial career choice. Fay too found that she enjoyed her

change choice of teaching and was confident that if she continued to work well, she would gain

permanency and a professional occupational direction for life. Despite not initially intending to

change occupations, at interview Flynn was content that he had taken the redundancy and the

Federal Government scholarship to complete his change degree, and was very happy to have

worked within various Public Service bureaucracies in his change degree-related fields ever since.

Though Keith had settled well into his general practice partnership he expressed some regret

about not commencing medicine earlier. Doing so would have given him the time to specialise

before marriage and family, which he saw as incompatible with the rigorous demands of specialist

training. Kate too had some regrets. Despite her love of animals and long interest in veterinary

science, she felt “the course was really good but I still feel like I don’t know what I’ll be doing in

five years …I’m not ecstatic but I don’t hate it”. She has thought about “maybe doing a PhD or

something further in research in vet”, or maybe doing medicine which would “avoid the biggest

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issue we (vets) have which is financial... I feel like I’m struggling the most now…more than I ever

have in my entire life”. She said, “I enjoy the horse work”, but there is not a lot of that locally. Also

horse work involves reproduction and some specialists divide their year between the northern and

southern hemisphere breading seasons according to Kate. She felt that all veterinarians find small

animal work emotionally stressful especially the conversation when “you say: it’s going to die if

you don’t give me this money.”

At interview, several years after having spent five difficult years completing the Juris Doctor

qualification while supporting her family, Sue had still not really used the qualification. She felt

she wouldn’t. Brian and Melanie were still studying. Brian was really enjoying his course and very

confident about the career outcomes. Melanie was finding her study/work regime demanding but

appeared to be enjoying the course material. She was a little anxious about breaking into the

accounting professions on graduation but was determined to give it her best shot.

In summary, in several cases, although the outcomes varied a little from participants’

original intention, there was little expression of regret at having taken the step. However, Keith felt

that late entry to medicine had limited his outcome options regarding specialisation, and Kate was a

little disappointed at veterinary science practice, and more particularly, at the poor level of

remuneration. Most participants were generally happy with the outcomes of their change decision,

and those still studying were very optimistic about their future careers.

7.3 Chapter Summary: Phase 2 Data Analysis

Of the nine participants, all had returned to study in a changed broadfield; eight considered

their intent was for disjunctive occupational change and one wanted to add a new skills and

knowledge set to complement her existing expertise. Participants embarked on idiosyncratic

approaches to both initial and change decision-making and consulted with family and friends to

varying degrees, but sought little professional assistance. The motivations for changing were

mostly related to disappointment with the outcomes of their initial qualification (mostly practical in

nature), some of which appeared to be related to their initial decision-making processes. Some

participants attributed initial decisions to immaturity at the time. Despite the intention of selecting a

new broadfield to avoid these sources of disappointment and disillusionment, there was surprisingly

little evidence of comprehensive exploration of change alternatives, or even of the broadfield

selected. Several participants were also critical of perceived poor preparation at university for

professional practice during their initial degree.

The important concern to be managed about the change decision was related to funding all

aspects of the change process, including course costs, living expenses, and coping with perhaps

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lowered income on change course completion. The financial concerns appeared to interact with

life-stage issues, and their joint influence on change decisions and change course choice,

particularly study mode, were also stressed. There was general agreement that participants felt they

were more dedicated students and coped better with the demands of university as mature-age

students. Mature age was also seen to be an asset in some aspects of graduate job seeking after

broadfield change, but a disadvantage in others.

In an exploratory study with 9 participants, the focus was on the richness and depth of the

data quality to capture the perspective of changers regarding explanations for the quantitative

findings of low levels of disjunctive change in knowledge occupations. The intent was that the

qualitative findings would indicate directions for future research on the issues involved in making a

disjunctive occupational change, in view of the predictions of future increased mobility so that

reaching general saturation in this small phase was not sought. However, all participants had

undertaken a similar task: returning to study in a change broadfield, and there were several

important aspects of their experiences where volunteered responses were in agreement ,which did

suggest saturation on those issues. On the themes of consultation and financial (issues) and the sub-

themes of the effects of life stage (issues) and problems with employment, all participants identified

similar issues as evidenced by the quotes from interviews, so that increasingly little new

information on these themes was added with each further interviewee. Most participants also

highlighted the effect of immaturity/maturity. Relatedly there was also a consensus among

participants that they felt more dedicated to their study and interested to learn when undertaking

their change qualification. Thus, though overall saturation was not sought in this small exploratory

phase of the research, given the many possible aspects of studying a change broadfield, agreement

was evident on most major issues involved in their common change undertaking.

The majority of participants were satisfied with the outcomes of their change decision.

Further, most considered the change disjunctive, but were able to describe areas of skills and

knowledge transfer.

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Chapter 8

Discussion

This chapter discusses the results presented in Chapters 4, 5, 6 and 7 in relation to the

research aims, and will be structured according to the research questions.

Question 1. What percentage of students who already had at least a Bachelor Degree and

who were undertaking further tertiary study chose to study in a different field from that of a

previous qualification?

Question 2: What were the patterns of change with respect to particular fields?

Question 3: Were staying or changing patterns related to particular demographic or

contextual factors?

Question 4: Why did some individuals with at least a Bachelors Degree qualification choose

to study in a field different from that of previous study?

Question 5: How did demographic and contextual factors influence the decision to change

broadfield or affect outcomes of the change?

First, results related to Questions 1 and 2 on broadfield changes and patterns of change

from the quantitative phase of the research will be discussed, followed by findings from the

qualitative phase (Question 4). Next questions 3 and 5 will be addressed using results from both the

quantitative and qualitative analyses. The chapter will conclude with discussion of the results in

relation to theory.

8.1 Broadfield Changes and Patterns of Change

Broadfield changes will be presented initially from an overall perspective, followed by a

progressively more detailed breakup of patterns of change by groups of broadfields, then individual

broadfields. The nature and direction of such changes will then be discussed within the context of

the skills-based nature of mobility and the extent of possible disjunctive change.

8.1.1 Percentage changing broadfield (Question 1). Of the more than 97,000 domestic

respondents to the 2008 graduate destination survey, the previous qualifications of approximately

33,000 were recorded in the secondary data set available and approximately one-third were at least

at Bachelor Degree level. Thus over 11,000 respondents had completed extra credentials.

Significantly more than half were up-skilling in the broadfield of their initial qualification,

supporting Roksa and Levey’s (2010) finding of a positive correlation between individuals’

broadfields of undergraduate and postgraduate study. Conversely, significantly fewer than half had

completed their further credentials in a different broadfield, consistent with the contention that

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highly educated individuals tend to have low occupational mobility (Elliott & Lindley, 2006;

Farrell, 2009; Shniper, 2005; Sicherman & Galor, 1990; Super, 1980b).

In the context of career models (e.g., traditional and boundaryless models), the patterns of

change between broadfields and broadfield groups will be discussed next, followed by an analysis

of change patterns in relation to generic and occupationally specific qualifications.

8.1.2 Patterns of broadfield change. (Question 2). The present results are in keeping

with previous findings that business and education (Chambers, 2002; Chong & Goh, 2007;

Coladonato, 2013) are popular broadfields for second study, whereas the STEM broadfields of

science (Stinebrickner & Stinebrickner, 2014), and engineering (Langford, 2006) are unpopular

(low change-in). The apparently large differences between change-in numbers for education and

science in the present study cohort, however, may have been inflated. Within the data-set it was not

possible to identify and thus eliminate pre-planned, consecutive degree programmes (a generic

course followed by a specific-skill course) for a specific occupational outcome, such as medicine

(B.Sc. /MBBS) or teaching (B.A. /Dip. Ed; B.Sc./ Dip Ed) The effect of such pre-planning could be

to increase both the numbers changing into education and the numbers changing out of science.

In Chapter 5, broadfields were divided into three groups (see Table 8.1) according to their

change ratios (changes-out/changes-in), to enable comparisons and categorisations of broadfield

changes. In the next section, these change patterns will be interpreted in the context of the literature

to clarify the skilled-based nature of attempted mobility.

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Table 8.1

Groups of broadfields

Group Change ratio Size of recent study cohort

compared with past study cohort Broadfields

Group 1 < -20% recent study cohort < 80% of past

study cohort

Science

Engineering

Creative Industries

Group 2 >20% recent study cohort >120% of

past study cohort

Education

Business

Health

Architecture

Group 3 -20% to + 20% recent study cohort between -

20% & +20%

Society & Culture

IT

Agticulture and

Envitonmental

Science

8.1.3 Group 1 broadfields: science, engineering (STEM), creative industries. With the

projected future demand for technical knowledge and expertise (Civic-Impulse, 2016; Commission,

2007), it is important to understand the attrition, and the unpopularity of science and engineering as

second-study choices, and to consider mobility and career pathway options for science and

engineering graduates. The relatively high attrition rate from science is also in keeping with

American findings on undergraduate majors that, “…relative to other majors, students are both

more likely to leave science (if they started in science) and are less likely to change-into science (if

they started in a major other than science)” (Stinebrickner & Stinebrickner, 2014, p. 467).

Entry to tertiary STEM programmes usually requires prerequisite scientific knowledge, but

increasingly students in the later years of high school are not studying science (Giles et al., 2009).

Therefore for university graduates who may otherwise consider a second study in STEM fields, the

academic difficulties of the disciplines (Kidd & Naylor, 1991), and the opportunity cost (Baird,

2012) of achieving the prerequisite knowledge could be serious disincentives. Poor employment

and career-development options for graduates, resulting in attrition in STEM occupations, have

been noted. “The cost-benefit ratio of a SET (STEM) career in Australia is steadily worsening”

(Langford, 2006, p. 3). In general then, the financial return on a STEM type degree in Australia is

not commensurate with the academic difficulty of the course and the costs of achieving the

qualification, or with the level of expertise and responsibility required in degree-related positions.

In engineering, the employment prospects are far better for graduates in degree-related jobs

than for those with science degrees (Coates & Edwards, 2009). The overall nett loss from

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engineering in the 2008 graduate cohort is in keeping with Langford’s (2006) observations that

many leave but few move into the profession because of poor wages, low-quality supervision and

lack of career structure. Further, the only mobility pathway for career advancement within

engineering in Australia is to gain expertise in management (Langford), which 50% of the current

respondents leaving Engineering actually did. This change pathway is not new, however.

Williamson (1979) found that the most common occupational changes undertaken by engineering

and science graduates were to management and teaching. However, neither of these change

patterns necessarily indicates a disjunctive change-out of STEM fields.

The highest proportion of present returnees who changed from engineering into business

was in the 30 to 39 years age-group. At this life-stage, consolidation of engineering expertise

should have taken place, and workers may look to ‘add-skill’ for career advancement, though in

Kelan and Jones’s (2009) study, established professionals began a Masters of Business

Administration (MBA) a little younger in the 26-35 age range. That engineers undertake business

studies for advancement in an engineering career also supports Shniper’s (2005) findings in the

USA that engineers have one of the lowest levels of occupational mobility. The mobility obtained

from add-skilling would likely be between jobs or job levels in the same profession, rather than

disjunctive occupational change.

In Australia, science graduates were more likely than graduates in other fields to return to

study in the first year after graduation, including pre-planned consecutive degree programmes in

education or medicine (Coates & Edwards, 2009). However, even if not pre-planned, these two

mobility options would mean continued utilisation of scientific knowledge and so could not be

considered as true disjunctive change.

Poor degree-related employment opportunities in the Australian labour market for Bachelor

of Science graduates have been noted (Coates & Edwards, 2009), and such graduates tend to

relocate overseas to find acceptable employment (Giles et al., 2009). However, in an Norwegian

study (Støren & Wiers-Jenssen, 2016), the demand for science and technology graduates has been

found to vary with business cycles. Relatively low remuneration on qualification (Langford, 2006)

would make recouping the opportunity cost of re-skilling in science or adding science to an existing

skill-set, problematic (Arnett, 2000). Thus if an individual did not initially choose science subjects

in high school, the likelihood of the STEM courses being a future mobility option would seem to be

very low.

Given the projected future demand for STEM qualifications, it is somewhat surprising that

remunerative returns and career development options are discouragingly low in Australia, and thus

students are less likely to be attracted to STEM fields. Perhaps it would be more useful to target

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specific STEM disciplines, particularly digital technology, to equip graduates with relevant

underpinning knowledge and skills for this so called “digital age” (World Economic Forum, 2016,

p. 9), referred to as the 4th

industrial revolution (World Economic Forum, 2016).

The high difficulty level of STEM type courses is recognised internationally (Arcidiacono,

2004; Frank & Walters, 2012; Reimer, Noelke, & Kucel, 2008), and graduates are valued often for

their perceived intelligence (Reimer et al., 2008). In circumstances where graduates are employed

on the basis of personal qualities and general ability levels rather than specific skills-sets, the career

outcomes of STEM qualifications would mirror those of the more successful generic degree

graduates in terms of higher job and occupational mobility options and steeper career trajectories

(Roksa & Levey, 2010).

The employment outlook for creative industry graduates in terms of numbers of vacancies

and remuneration is reportedly bleak. Arguably the high change-out and low change-in rates could

be explained in terms of these potentially poor outcome (Adamuti-Trache et al., 2006; Frank &

Walters, 2012).

8.1.4 Group 2 broadfields: business; education; health; architecture. Business is a

heterogeneous field of study (Rothstein, 1980) and differs from most other occupationally specific

fields in that the knowledge and skills in many specialisations, particularly management, have

general applications in most industries. . Other specialities (accounting and finance) have been

found to be more akin to specific occupational groups in the closer relationship between such

qualification and employment (Jackson & Wilton, 2016). However adding a business qualification

would be likely to improve job mobility and adaptation options, which would contribute to the

popularity of business courses as second-study choices (Baird, 2012), as found in the current

research.

The highest proportion of the overall changes into business was in the 30 to 39 age-group

(46% of this age group). As employment opportunities for new graduates in the private financial

and business sectors tend to decline sharply with increasing age (Egerton, 2001; Purcell, Wilton &

Elias, 2007), it is possible that such changes into business could be add-skilling for promotion in the

same organisation, or to improve competitiveness for job movement between organisations in the

same occupation. Of all broadfields, engineering and IT had the highest proportion of their change-

out cohort changing into business (approximately 50% each), particularly into the management

narrow-field. As discussed above, such add-skilling is a recognised career development pathway

for engineers (Langford, 2006; Lewis & Thomas, 1987). Adding one compatible specific-skill set

to another would add to rather than diminish an individual’s human capital, in contrast to a

disjunctive change. Job and job-level mobility would be enabled without surrendering the human

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capital accumulated from the initial degree and from expertise developed from initial-degree-related

employment.

With education, unlike other broadfields, the proportion of each group ‘changing in’ was

consistent across most age groups, indicating that education was seen as a viable second-study

option up to age 60, offering job and occupational mobility options in later career. Females

dominated the numbers changing in up to age 50, after which there were no gender differences in

preference for education. These results are in keeping with the success of mature-age recruitment

of second-career teachers in the USA (Kleiner, 1998). Changes into secondary teaching where

previous expertise could constitute teaching specialities may well be add-skilling for job mobility,

but other changes, particularly into primary teaching, could be considered disjunctive.

Health and education as second-study broadfields differed from business in that many who

changed-in chose qualifications that lead to “classical professions” (Schomburg, 2011, p. 56), or to

other occupations with recognised professional identity and possibly registration. Such occupations

ease initial entry to the labour market in degree-related employment (Roksa & Levey, 2010). The

mandatory qualifications for bounded, high-initial-investment, gatekeeper-protected knowledge

occupations in health make inter-occupational transition at level difficult for career self-managers.

Therefore the ease of occupational change in the predicted ‘new careers’ that is implied in the

mobility models would have limited application to this large group of knowledge occupations

(Pitcher & Purcell, 1998; Weeden, 2002).

Health was the most static of the eight largest broadfields, with the highest percentage of

change-ins from the under 30 age-group and the lowest change-out rate. This change-in pattern

perhaps reflected the numbers of respondents on pre-planned, consecutive degree programmes for a

specific skill occupation (e.g., medicine). These change patterns for health support Shniper’s

(2005) inclusion of health in her least-mobile occupational group.

8.1.5. Group 3 broadfields: society and culture; information technology (IT);

agriculture, environmental and related studies. Society and culture and IT will now be

discussed. The very small numbers in agriculture, and environmental and related studies precluded

further analysis in those broadfields.

Society and culture was the largest and most diverse broadfield, incorporating generic

qualifications such as arts, humanities, languages and social science, plus some professional

qualifications offering occupationally specific skills such as psychology and law. The largest

number of changes-out was from the generic arts/social science narrow-fields, which also attracted

virtually no changes-in. As mentioned previously, some of these changes out of generic courses to

occupationally specific courses such as education, may have been pre-planned rather than the result

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of difficulties finding employment after generic qualifications (Frank & Walters, 2012). The

changes into the society and culture broadfield were mainly into the specific-skill narrow-fields

such as law and psychology. In addition, within the broadfield there were changes from the generic

narrow-fields (e.g., arts and social sciences), to the specific-skill narrow-fields, particularly

psychology and law.

IT, like other technical fields, is considered a specific-skill field. IT skills have specific

applications in most industries and therefore feature as useful tools in many different occupations

(Baruch, 2010). The high change-in proportion was thus understandable in terms of respondents

adding specific IT skills to enhance their existing expertise in, for example, engineering. Though

both IT and business skills are applicable to many occupations and industries, their contribution to

career trajectories would likely differ. IT skills would enhance engineering practice but, unlike

adding a management qualification, would not necessarily equip practitioners for moving out of

practice into a supervisory-type position. As with engineers, half of those changing out of IT

completed qualifications in business, so the proportionally high IT change-out figures could be add-

skilling for movement into management.

With IT, only one-third of the past study cohort returned for further formal IT study, which

is understandable given the rapidly changing nature of the discipline. An initial entry-level

qualification in IT is a pathway to employment as an IT professional (Adams & Demaiter, 2008).

Employers are interested in credentials as an indicator of the ability to learn in this area. Informal

workplace and other ongoing learning appear to be more functional than further formal study in

dealing with the rapid vocational-skills obsolescence typical of IT (Adams & Demaiter, 2008).

8.2. Identifying possible types of change

Overall it was not possible to determine accurately the extent of probable disjunctive

change-intent, but there were indications of some patterns of change that reflected traditional career

trajectories (e.g., engineering or IT to business), suggesting add-skilling for job mobility and career

development rather than re-skilling for disjunctive change. Other patterns suggested a perceived

linking of two fields. For example, there were changes between business and law, in both

directions. Such patterns of occupational linkages between some broadfields suggest that much of

the extra credentialing involving broad-field change may not have been undertaken with the intent

of disjunctive change. These findings suggest that mobility in knowledge occupations is more

likely to be between jobs or job levels, rather than occupations, consistent with the observations of

Parrado et al. (2007). In their study, 90% of the occupational changes of professional workers were

between occupations that required similar skills (p 443). The patterns of change from engineering

and IT to business in the present research are in keeping with Parrado’s example of change patterns

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from professionals to managers. This tendency to ‘add-skill’ rather than re-skill may indicate

perceived career development pathway options (and limitations) in knowledge workers. Next, the

patterns of broad-field change will be considered according to the generic/specific-skill dichotomy.

8.3. The Generic Versus Specific Skill Dichotomy

Specific-skill degrees ease initial entry to the world of work in degree-related employment,

but also produce flatter career structures and thus very limited opportunities for occupational

mobility (Adamuti-Trache et al., 2006; Drewes & Giles, 2001; Frank & Walters, 2012; Jamieson,

Sabates, Woodley, & Feinstein, 2009; Roksa & Levey, 2010). In comparison, generic degrees, such

as liberal arts degrees, may make initial workforce entry in degree-related work more difficult, but

can provide better long-term career outcomes and higher levels of occupational mobility (Borghans

& Golsteyn, 2007; Drewes & Giles, 2001; Markey & Parks II, 1989; Roksa & Levey, 2010;

Shniper, 2005). Given conflicting evidence about the relative benefits of specific-skill verses

generic-skill qualifications, the three broadfield groups identified for levels of change-in/change-out

will be considered according to that dichotomy.

Group 1 broadfields. Of the three broadfields that lost individuals and did not attract many

changers-in (science, engineering, and creative industries), engineering is the only one considered

to be in the specific-skills category (Pitcher & Purcell, 1998; Shniper, 2005). General science is

classified as generic (Coates & Edwards, 2009; Roksa & Levey, 2010), in that a bachelor-level

general science degree may not lead directly to an obvious occupational pathway. For example, in

2008, 67% of new science graduates in Australia may have been using some science expertise but

were not working in industries classified as scientific, with 13% initially working in retail and 5%

in hospitality (Coates & Edwards, 2009). Qualifications in creative industries, though technically

specific-skills fields, tend to be included with humanities and social science because of similarly

lower degree-related employment opportunities and comparatively low average earnings (Adamuti-

Trache et al., 2006; Frank & Walters, 2012).

Group 2 broadfields. The four broadfields that both maintained participation and attracted

broadfield changers (business, education, health and architecture), have all been referred to as

occupationally-specific (Frank & Walters, 2012; Roksa & Levey, 2010). Degrees in these

broadfields have direct application to employment closely related to the skills and knowledge in

their qualification.

Group 3 broadfields. The third group of broadfields (society and culture, information

technology, agriculture, environmental and related studies), all both lost and gained participation.

Society and culture comprises a mix of generic and specific-skills qualifications, and the changes

tended to be from the generic fields into the specific-skills areas. IT, like other technologies, is

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considered a specific-skill field, and the reasons for little up-skilling but high change-in and change-

out proportions have been discussed.

8.3.1. The change direction: generic to specific-skill courses. The direction of change

appeared to be primarily away from generalist qualifications to occupationally-specific degrees or

between pairs of specific-skills qualifications. It is possible that in moving away from generic

qualifications, the changers were focussed on acquiring marketable specific-skills sets from their

change qualification to ease their entry into the workforce (Adamuti-Trache et al., 2006; Carless &

Arnup, 2011; Drewes & Giles, 2001; Frank & Walters, 2012; Goyder, 2014; Markey & Parks II,

1989; Roksa & Levey, 2010). Such a motivation would support Carless and Arnup’s findings

(2011) that job security was the prime motivator for occupational change. As noted, the extent of

such moves as field-of-study changes may have been inflated by pre-planned moves into

occupationally-specific courses. Alternatively some respondents may have chosen not to specialise

too early for later career benefit (Malamud, 2011; Rothstein, 1980). Completing a generic degree

initially, to allow time to explore abilities and preferences before choosing a speciality, has been

linked to fewer disjunctive changes later (Malamud, 2011).

In summary, changers predominantly moved away from generic-type qualifications into

occupationally-specific degrees, or combined two specific-skill qualifications. The move to

specific-skill qualifications suggests a preference for knowledge-bounded occupations. Such

occupations offer more job security (Carless & Arnup, 2011) but possibly low mobility options and

flatter career trajectories, in contrast to the less secure but possibly steeper trajectories (Roksa &

Levey, 2010) of the self-managed, more flexible, ‘new careers’.

8.4 Why Participants Returned to Study in a Different Broadfield

Question 4. Why did some individuals with at least a Bachelors Degree choose to study in a

field different from that of previous study?

In this section, the response to Question 4 is derived mainly from the stories of participants

in the qualitative phase of the study. The decision-making, course experience, and world-of-work

antecedents of their decisions to return to study in a different broadfield are examined in turn, and

the reasons for returning summarised.

8.4.1. Ineffective decision-making processes. Though six of the nine participants were

retrospectively critical of their initial decisions, they offered less criticism of their actual decision-

making processes, and followed similar ad hoc approaches when making their second choice

decision. Several interactive factors could be seen as playing a role in participants’ “initial poor

occupational decisions” which contributed to their eventual action to change occupations (Longhi &

Brynin, 2010, p. 664).

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No professional consultation. The idea that career guidance was particularly important in

assisting people with occupational decision-making has seeded a body of literature on the processes

and importance of career guidance (Amundson, Borgen, Iaquinta, Butterfield, & Koert, 2010;

Bikos, Dykhouse, Boutin, Gowen, & Rodney, 2013; McMahon, Patton, & Watson, 2015; Meijers,

Kuijpers, & Gundy, 2013; Savickas, 2008; Watson & McMahon, 2006). However, a recent study

suggests that career services in Australian schools are under-resourced (McKenzie, 2015). Some

participants had career guidance classes at school, but overall, professional, one-on-one career

advice was seldom sought for assistance with initial or recent decisions, even though the return to

university involves considerable cost and life-stage disruption. A stigma in seeking career

counselling (Ludwikowski, Vogel, & Armstrong, 2009) may be partly relevant here.

Failure to seek career advice is not unique to Australian students. For example, in a UK

study, the majority of undergraduate students had not sought advice and had only a vague idea of

possible occupational outcome options (Pitcher & Purcell, 1998). Similarly, behaviour of

participants in the present study was in keeping with indications that first-year Australian university

students show little interest in pursuing a group or one-on-one career counselling session (Walck &

Hensby, 2003). As with previous studies (Walck & Hensby, 2003; Walker, 2006), present

participants relied on the opinions of parents and friends when making their initial decision. For

some participants, these sources of advice were even more extensively called upon regarding the

decision to change, and the selection of the change broadfield. Even though parents are widely

regarded as important influences on occupational direction and course selection, the extent,

accuracy and recency of their knowledge of occupational information or of course offerings in

higher education is uncertain. Similarly, though most teachers do not have specialist knowledge,

Amundson et al. (2010) found that students relied on teachers for career guidance, as did one of the

current participants.

Failure to investigate outcome options. As described in Chapter 7, most participants’

initial decision-making processes were rather haphazard, with several using a trial-and-error

approach, while others relied on subject preferences at school, tertiary entrance eligibility, known

classical professions, or family precedents, with very little longer-term career consideration or

appraisal of personal suitability for the occupations. By contrast, for USA students from all

disciplines, expected earnings factor particularly strongly in their choice of majors (Arcidiacono,

2004; Montmarquette, Cannings, & Mahseredjian, 2002; Webber, 2014), though factors such as

ability, preference, information, socioeconomic background, gender and race, have some influence.

While acknowledging that major choices should not be based primarily on economic

outcome prospects, Webber provided evidence on “large disparities in lifetime earnings…between

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college majors” (2014, p. 22). He argues that with struggling labour markets and rising education

costs, remuneration must be considered in career decision-making, so that choice of major “depends

decisively” on expected relative earnings (Montmarquette et al., 2002, p. 554). Present participants

did not appear to base their initial decisions on expected earnings; in fact, quite the contrary. Kate

deciding to major in film and television where she knew outcome opportunities were rare. Thus the

style of participants’ initial career decision-making processes supported previous conclusions that

high school students have very little idea of the world of work and so tend not to make stable,

rational, career plans (Lewis & Thomas, 1987; Walker, 2006).

Immaturity. As in past research, many of the current participants reflected “in hindsight on

their naivety or immaturity at the time of that (initial) decision” (Howes & Goodman-Delahunty,

2014, p. 68). Participants believed they lacked self-knowledge, were unready to commit to further

serious study and afraid of professional responsibility, so thus unprepared to make important

decisions (Gati, Krausz, & Osipow, 1996).

In summary, as with the participants in Lewis and Thomas’s study (1987), few of the current

participants had thought through or investigated the potential occupational outcomes of their initial

degree choice in any depth; most acknowledged the role that immaturity played in that decision, and

thus the eventual need to move into another broadfield. Next, participants’ disappointments with

aspects of their initial degree and the outcomes, which resulted in their decision to undertake

disjunctive change, are discussed.

8.4.2. Dissatisfaction with initial degree. Dissatisfaction with the initial degree stemmed

from a variety of issues: poor emotional preparation for professional work, poor technical

preparation, and poor awareness of industry skill requirements. On graduation, several felt

unprepared for practice because of deficits in technical knowledge and skills, and/or in emotional

preparedness for professional relationships, especially in dealing with client trauma and stress. In

particular, the female law graduate felt totally unprepared to cope with the emotional and physical

workload of family law practice, to the extent that her level of distress necessitated the disjunctive

move. Stress in lawyers and law students is a recognised concern for the profession, and previous

research has suggested that law schools have a role in properly preparing and supporting students

(Bergin & Jimmieson, 2015; Field, Duffy, & Huggins, 2013; Sugarman, 2014).

Improved work experience during degree study, as suggested by Minten and Forsyth

(2014), may have helped those who found their graduate level of professional expertise not

acceptable to industry. For example, the human movements graduate may have benefitted from

more opportunities to increase her technical skills. However, the film and television graduate found

that even adding her experience in Los Angeles, her degree content was not relevant to industry

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tasks, making it very difficult to find work in an industry where employment opportunities were

relatively scarce.

In summary, participants with initial degrees in the helping professions felt under-prepared

technically and/or personally for professional practice. Another participant felt her course did not

equip her with skills required by employers in her major field of study.

8.4.3. Dissatisfaction with occupational outcomes. In keeping with previous findings

(Kucel & Vilalta-Bufí, 2013), dissatisfaction with the occupational outcomes from initial degrees

was the prime reason that individuals regretted their initial decision, and returned to study a change

qualification. Their dissatisfaction stemmed from employment issues including limited availability

of employment and consequently poor job security (Carless & Arnup, 2011), dislike of the work

(Borghans & Golsteyn, 2007), work-related stress, and moral issues (Bergin & Jimmieson, 2015;

Minten & Forsyth, 2014).

Limited availability of secure employment. The qualitative results agree with Carless and

Arnup’s findings (2011) that lack of job security was a predictor of actual occupational change.

The generic to specific-skill change patterns in the quantitative data also support the perceived

importance of job security among changers. For different reasons, three participants were affected

by limited degree-related employment opportunities. There was little demand for the more general-

type skills Kate had developed from her film and television major (Roksa & Levey, 2010).

Similarly, sports/fitness-type industries without professional recognition had only rather vague

skills requirements, as Fay encountered after her sport and recreation degree. In both cases

participants changed from degrees without defined occupational outcomes to registered professions

(veterinary science and teaching).

To her surprise, the pharmacy graduate also found limited vacancies in her registered

profession because of the sudden increase in the number of places in pharmacy degrees, resulting in

a glut of graduates. In view of the experiences of several participants, Chester’s contention (2014)

that stronger links are needed between government-funded education systems and the labour market

regarding course funding, warrants consideration.

Dislike of the nature of the work or future prospects. Much has been written about

matching personality to occupation (Carless, 1999; Holland, 1973; Olitsky, 2014). Two

participants who were successfully employed in degree-related engineering positions changed to

broadfields which they saw as more people-oriented (e.g., medicine), to reduce the emotional and

social isolation they found in engineering. Their traditional career trajectory prospects from

engineer to manager of engineers seemed distant and uninviting. Their motivations for change thus

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centred on the importance of “meaningful engagement: a desire to make a contribution to others or

to the world” (2010, p. 324), and thus job satisfaction (Carless & Bernath, 2007).

Similarly, the pharmacist found the nature of long-term employment in community practice

repetitive and uninviting (Seston, Hassell, Ferguson, & Hann, 2009), with future prospects for

career development virtually non-existent. She felt that her disjunctive change from a regulated,

role-specific profession to another regulated specific occupation (accounting) with direct relevance

to many industries, would greatly widen her job opportunities and scope for future career

development.

Moral issues and personal values related to the profit motive in a sports-related industry, as

described byMinten and Forsyth (2014), was a source of occupational dissatisfaction, and a

motivation for change by the current human movement graduate.

Stressful working conditions. Women’s vulnerability to harassment in the work place is

well documented (Cesario, 2013; Cogin & Fish, 2009; Nelson & Burke, 2000; Srivastava, 2004;

Yousaf, 2014). It has been shown that workplace harassment can seriously impact the emotional

and professional well-being of women (Yousaf, 2014) and lead to workplace withdrawal (Cesario,

2013), which was the experience of two participants. In addition to sexual harassment, the female

lawyer in family law faced long hours, and very emotionally-stressed clients (Sugarman, 2014),

with no support from colleagues or a mentor. Both harassed participants were interested in the

professions they chose, and found jobs easily, but later opted for disjunctive change to escape the

levels of stress they were experiencing at work.

8.4.4. What participants hoped to achieve by changing. Rather than add-skilling for a

managed transition or adaptation as part of an ongoing career development plan, seven of the nine

interviewees intended the outcome of their recent qualification to be a disjunctive change to an

occupation that offered future job security and satisfaction, and avoided the problematic issues they

had confronted in the past. This finding is more in keeping with Canadian findings that

remuneration was only a secondary factor in occupational choice decisions (Amundson et al.,

2010), than with the contention that financial gain was the prime mover in occupational mobility

(Frank & Walters, 2012).

In summary, change decisions resulted from job insecurity, dislike of the occupational role

or the environment of practice, and emotional stress from the nature of the work or from

relationships within the organisation, some of which may have stemmed from poor initial-degree

directional choices, and little investigation of outcome options. In the next section, factors related

to the change decision itself and the process of undertaking the change are considered. Related to

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the fear of job insecurity, the only variable mentioned by all of the few previous studies of

disjunctive change is financing the change.

8.5 Factors Relating to the Decision to Change and the Change Process

Question 3. Were staying or changing patterns related to demographic or contextual factors?

Question 5. How did demographic or contextual factors influence the decision to change

broadfield or affect outcomes of the change?

In this section, quantitative and qualitative results relating to the contextual factors of age,

gender and financial issues are discussed. Their influence on the decision to change or not change

broadfield, and the nature of the change, are then considered.

8.5.1. The Interrelationship between age and changing broadfield. The present results

indicated that in absolute numbers, individuals were more likely to return to university study early

in their working life, up to age 30 (median age 32), than at later ages. This finding supports

previous research that workplace mobility is highest before age 30 (Coelli et al., 2012; Huang &

Sverke, 2007; Kambourov & Manovskii, 2008; Markey & Parks II, 1989; Shniper, 2005; 2010;

Stumpf, 2014). In contrast, older persons were less likely to change occupations or industries,

possibly because of their usually higher remuneration from accumulated experience, and expertise

(Parrado et al., 2007). Hence, older people generally derive much less benefit from changing

occupations (Shniper, 2005), and have limited time to recoup the opportunity cost of the change.

However, considering percentages rather than absolute numbers, the relationship between

returnee age groups and changing or not changing broadfield of study was somewhat different.

Though actual numbers in each age group of returnees decreased greatly and consistently with

increasing age, there was a steady increase in the proportion of returnees in each age group who

changed broadfield with increasing age. For example, 42% of the 20-to-29 group changed

broadfield, while 53% of the 30-to-39 group changed. This tendency was unexpected, given Neal’s

findings (1999) that workers undertook less complex changes with increasing age; that is, they were

more likely to change employer than to change both employer and occupation. The present results

suggest, however, that specialisation is more likely to take place as individuals are establishing

themselves in their initial occupational field, rather than later in life.

As the decade between 20 and 30 years of age is emphasised as the time of highest mobility

(Markey & Parks II, 1989; Shniper, 2005), the skills-and-knowledge-based nature of that mobility

requires clarification. Though the actual numbers of returnees undertaking broad-field change were

the highest in the 20-to-29 age group, they were proportionally only 42% of that age group, with the

remaining 58% up-skilling in their existing area of expertise; that is, they did not change broadfield

of study. Of the 42% who changed, it is not possible with the present data to understand their

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occupational intentions, but there are several possibilities: disjunctive change of occupation, add-

skilling to complement existing expertise for career advancement in the initial occupational area, or

completion of a pre-planned program. However, the 58% who up-skilled, plus those who add-

skilled, were likely to be specialising in the same occupational field; so mobility outcomes from

their recent qualification would be between jobs, rather than between occupations. Therefore, the

well-established high mobility in those aged between 20 to 30 in knowledge occupations is more

likely to be up-skilling or add-skilling for advancement in the existing occupation, than reskilling

for disjunctive occupational change.

The interrelationship between age, particular broadfields, and change. The age at which

individuals returned to study and changed broadfield was related not only to the broadfield they

changed into, but also the broadfield they were changing out of. For example, the change into

business was proportionally highest in the 30-to-39 age group, which may be indicative of a

planned career move into management after the establishment of occupationally-specific expertise.

“Separation from a previous career….to make possible re-entry to the same field at a higher level”

(Kelan & Jones, 2009, p. 564).

Similarly, as IT and business share a complementarity with other broadfields such as

engineering, it is not surprising that there was a similar but less marked pattern of change-into IT

between the ages of 30 to 39. In comparison, health had the youngest change-in cohort (perhaps

related to pre-planning), and most of the changes into and out of STEM broadfields (other than

changes into business and IT) also occurred in the youngest age group.

Interestingly, education and society and culture had more change activity (both in and out)

in older age groups than other broadfields. With education, these results are in keeping with the

current picture of teacher turn-over (Laming & Horne, 2013; Pinnington-Wilson, 2004; Williams,

2005). Teaching is seen as an occupationally-specific change option for older changers, and as an

accepted occupational change pathway where maturity and experience could be seen as strengths

(Anthony & Ord, 2008; Lee, 2011; Williams & Forgasz, 2009). Similarly the later changes into

society and culture were to various narrow-field professions, particularly psychology and welfare,

suggesting a perceived possibility of employment opportunities for mature-age graduates (Boeren,

Nicaise, & Baert, 2010; Egerton, 2001).

Thus most broadfields had their own pattern of changes in and out, related to age. In the

next section, age-related issues involved in disjunctive change are discussed with reference to the

experiences of the interview participants.

Age-related issues in opting for disjunctive change. Life-stage correlates of age, such as

family and financial responsibilities, have been shown to affect the freedom to change occupation

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(Howes & Goodman-Delahunty, 2014; Neapolitan, 1980). Combinations of life-stage issues make

mature-age disjunctive change decisions complex, as reported by several participants.

Maturity. Maturity had varying impacts on disjunctive change decisions. The negative

influence of a lack of maturity on the initial course choice and thus on eventual dissatisfaction with

occupational outcomes has been discussed. However, all participants felt that they were less

distracted, more dedicated and thus more effective students on return to university (Shimamura,

Berry, Mangels, Rusting, & Jurica, 1995). In addition, they believed that the intra- and inter-

personal maturity they had achieved made it easier to work within the system, sourcing the

available help with their studies (Aman, 2006; Swain & Hammond, 2011).

Unfortunately, the benefit of increased maturity did not necessarily extend to STEM fields.

The only participant who tried to add studies in the STEM subject of Mathematics in her change

qualification decided she had lost some ability in that difficult field. STEM study becomes more

difficult with increasing age (Kidd & Naylor, 1991), which may partly explain the unpopularity of

STEM fields among mature-age occupational changers. Generally most felt that maturity was an

asset in the education phase of the change, but there were some negatives in relation to the world of

work.

Similar to the experiences of mature-age new graduates in the study by Purcell et al. (2007),

two of the current participants had been concerned that they could be reporting to line managers

younger than themselves. The mature-age veterinarian graduate was being supervised by a younger

person and felt a little uncomfortable, but was coping with the situation quite well. It may be that

people changing from professional occupations where they have some status and expertise would

find the circumstance even more difficult. The oldest current participant who had considered

seeking employment with a large law firm on graduation echoed Purcell et al.’s finding that

“…mature graduates often find that they are caught in a ‘Catch 22’ situation because they have

maturity and experience but it is assumed that these render them inappropriate for ‘new graduate’

jobs” (2007, p. 75). Overall, maturity appeared to be an asset in the study phase of disjunctive

change, and in employment in some occupational areas such as education and welfare. However,

ability to cope with some more academically demanding fields such as STEM courses was found to

have declined, and finding employment in some fields was more difficult.

Age-related life-stage transitions. Second-study students have already successfully

completed a university qualification and so have an advantage over mature-age, first-time students,

but they still face the same life-course transitions found to impede the success of late entrants

(Roksa & Velez, 2012). Employment, cohabitation/marriage, and parenthood with all the

associated decisions, responsibilities, financial commitments, and personal adjustments, have to be

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incorporated into the development of what Super referred to as “multidimensional careers” (Super,

1980a, p. 282).

According to Super (1980b), the life space is divided between all roles (e.g., student, citizen,

worker, spouse, parent) and related activities (e.g., leisure pursuits, clubs, sport). If a knowledge

worker in this early career stage were to return to full or part-time study for either up-, add-, or re-

skilling, the existing roles and activities would be compromised, with possible social and financial

impact. An integral part of the transition to adulthood is the process of choosing and entering an

occupation (Wang, 2006), with a view to future career. Re-skilling for disjunctive change would

have an added disincentive of the financial impact of the opportunity cost of such a step. Thus

undertaking a disjunctive change in career direction would seem to be a major disruption to the

tasks of the first decade of adulthood for workers in knowledge occupations. As participant Fay

explained, returning to full-time study for disjunctive change, especially as a single person, was

akin to putting your life-stage development on hold in most respects, particularly financially.

Undertaking a mortgage on government benefits was out of the question.

Further, in the change decision-making process of all participants, consideration of life-stage

structures and tasks (Levinson, 1986; Savickas, 1997; Super, 1980a) was evident (Howes &

Goodman-Delahunty, 2014). It has been noted that the adult developmental tasks of life-course

transitions, such as settling in an occupation, marriage, and parenthood, have been pushed back to

the late 20s or early 30s because of the extended involvement in higher education (Arnett, 2000;

Bynner, 2005; Quintini, Martin, & Martin, 2007). This delay was evident with some of the current

participants.

The negative relationship between having children and changing career (Griffeth, Hom, &

Gaertner, 2000; Neapolitan, 1980) was reflected in the stories of several participants. Based on the

experiences of their mature-age, part-time student participants, Swain and Hammond (2011)

identified having children as an obvious constraint on the possibility of succeeding in a course of

study. However Carless and Arnup (2011) suggest that the number of dependents, rather than

children per se, is a better predictor of obstacles to career change. For Keith, the need to care for his

baby necessitated withdrawal from specialist training. Also, Fay reported that in her Diploma

cohort, a mother could not complete assignment work because of family demands, and had to

withdraw. As the breadwinner for her family (late teens), Sue could not afford to take the lower

paid judge’s associate or other entry-level law positions. Both the latter examples support the

contention of Still and Souter (2005) that when attempting further study, women are more impacted

than men by the child-care role, though Keith’s experience suggests that men’s career plans can also

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be impeded. The reports of current participants support previous findings that having dependent

children has negative effects on undertaking disjunctive change in knowledge occupations.

In addition to parenthood, relationship status may also influence career transitions. Though

Carless and Arnup (2011) claim that relationship status does not appear to affect the change

decision, the experiences of present participants were mixed, with positive and negative effects of

partnership. Accommodating a partner’s occupational constraints could limit outcome options for

the changer; for example, Cheryl’s and Lettice’s partners were both training for occupations where

they would have no control over their postings. By contrast, being in a partnership could also

provide financial backup if necessary in the disjunctive change process, as with Keith, Brian and

Melanie. Brian and Melanie both felt they were not dependent on their partner’s financial support,

but for Melanie there was financial backup for the mortgage if necessary; however, Melanie felt that

if still single, she would not have a mortgage and the associated financial pressure. Her situation is

an example of stress associated with the interactive effects of life-stage tasks and returning to

university study. For single people, being without a supportive partner may financially constrain

decisions (Robbins, 1978), as observed by participant Fay. In summary, the opportunity cost of

disjunctive change, together with changing domestic circumstances as part of life-stage progression,

may limit the freedom for disjunctive change and reduce outcome options.

Age and employability in a new profession. Many studies have pointed to aged-based

prejudice in hiring decisions (Fritzsche & Marcus, 2013; Greller & Simpson, 1999; Purcell et al.,

2007). However, in an Australian study, Jackson (2014) found the interaction of age and

experience had a positive impact on salary of graduates, but unfortunately she did not distinguish

between older age groups. Lettice, with a second study in business, strove for good grades in

anticipation of difficulties competing for jobs against traditional new graduates. Similarly, in the

study by Purcell et al. (2007), mature-age graduates, particularly those over age 30, had to face

subtle ageism and had much greater difficulty than traditional young graduates entering the labour

market in degree-related employment. Instead, they tended to be employed in lower-quality

positions. However two mature-age participants with business as a change qualification found

employment in graduate programs in the Public Sector, an option suggested by Egerton and Parry

(2001) and Purcell et al. (2007) as viable for mature-age business graduates.

Career development opportunities for mature-age entrants to a professional field vary

greatly depending on organisational structures and attitudes (Greller & Simpson, 1999). For

example, Sue felt she was unacceptable to large law firms as a mature-age new law graduate

because of her age and range of experience, which echoes Robbins’ contention (1978) that in the

USA, prestigious law firms have little interest in mature-age new graduates. Law as a second field

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of study had been completed by 248 respondents in the present quantitative study, whose future

degree-related employment options may be limited by this apparent attitude.

There were definite patterns of broadfield preferences chosen as change options by older

individuals. Some options were ruled out because of the length of training time. For example,

Lettice was advised against actuarial studies because of the length of the training period. Rather

than hard fields such as science (Bender & Heywood, 2011), the softer fields (Reimer et al., 2008)

were favoured, particularly welfare, and creative industries, in addition to education (already

discussed).

Welfare, unlike science, does not usually require specific prerequisite knowledge, so the

opportunity cost of changing-in would be lower. However, degree-related employment in

humanities and social science occupations (e.g., welfare) is not well remunerated, and mature-age

learners with less time to recoup costs may still not find such a change financially worthwhile

(Egerton & Parry, 2001). In contrast, an Australian study was more optimistic about recouping the

opportunity costs of mature-age change, particularly for females (Colgrave, 2006). Like education,

welfare is attractive to older changers where maturity is an advantage when competing for positions

(Egerton & Parry, 2001). The popularity of creative industries with respondents in their 50s and

60s suggests the motivation may be recreational with a view to retirement, rather than as a

preparation for occupational change.

8.5.2. Summary of age and age-related issues. The variable age and its correlates (such as

varying academic ability and life-stage issues) interact with attitudes in the world of work and with

the opportunity cost of change to influence decisions to change or not change broadfield, the

direction of any change, and the outcomes. Participants felt that their immaturity contributed to

poor initial decision-making and thus to disappointing outcomes, which motivated their decisions to

change occupation.

Most participants stressed the impact of undertaking a change qualification for disjunctive

occupational change on life-stage development and financial security in their late 20s and early 30s,

when such a change is most likely. As well, moving into new professions at progressively older

ages could present problems of acceptance and employability, depending on the particular

profession. As in previous studies, disjunctive occupational change between knowledge

occupations is relatively unusual, and financially and socially costly. In addition, the opportunity

cost of a disjunctive change increases with increasing age because of human capital loss.

8.6. The Interrelationship between Gender and Changing Broadfield

Although gender was not a primary focus of the present research, the quantitative and

qualitative results add some nuanced support to the apparent consensus that men and women tend to

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enact their careers in accordance with societal norms and role expectations (Sterret, 1999; Sullivan

& Arthur, 2006). The broad-field nature of the changes males and females made suggests different

expectations of career trajectories. In this section, gender differences in broadfield choice are

discussed, particularly in relation to gender role expectations.

The reason for the significant difference between the numbers of males and females that

met the selection criteria in the data set cannot be answered in the present analysis. However, the

number of females enrolled at universities has been gradually increasing relative to males, and the

female/male ratio in 2007 was 55/45% (Parr, 2015). Further, 15% more women than men in OECD

countries will complete a university qualification in their lifetime (OECD, 2014). It could also be

that female, mature-age, second-study graduates are more likely to complete questionnaires than

their male counterparts. Alternatively, the difference does raise the question of whether or not

women are 1.5 times more likely than men to return to study.

8.6.1 Gender differences in broad-field change choices. Consistent with the findings of

Parrado et al. (2007), the present quantitative results indicate that women are more likely to study in

the same broadfield as their previous qualification, that is to specialise, whereas men are more

likely to change their broadfield of study. The tendency to specialisation among women is contrary

to other findings of less linearity and more radical changes in women’s careers in that their

engagement with the world of work is often stop-start in nature because of their family roles

(Sterret, 1999). The difference from previous findings may be related to levels of occupation. The

present respondents, all with at least two tertiary-level qualifications, are likely to be in knowledge

occupations where occupational mobility at level is socially and financially difficult to undertake, as

current participants described.

From the quantitative findings, gender differences in broad-field second-study choices

persisted through the age groups until after age 59. Gender-linked change preferences were

interpretable in the context of previous research (Powell & Mainiero, 1992; Rindfuss et al., 1999),

suggesting that awareness and acceptance of traditional social/family roles and their implications

for workforce engagement motivated the choices of both genders. For example, a dominant change

pattern was males changing into business, often from STEM-type previous qualifications,

suggesting add-skilling for movement into managerial positions in their existing area of expertise.

Similarly, 70% of the changers into Information Technology were males, who may have been add-

skilling for career development. The predominance of men in STEM fields in the present data, is in

accordance with OECD figures (OECD, 2015). In contrast, females tended to change from generic

type courses into education, which attracted significantly fewer males, and also into health, and

society and culture. The attraction of education for women is congruent with Cooper and Davey

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(2011)’s summary of the teaching profession as a predominantly female occupation which is

family-friendly and offers flexibility.

What could be described as the ‘male to business’ pathway was prominent in the 30-to-39

age group when over 40% of male changers chose a second study in business, and continued

through the 40-to-49 age group Males changed into business at double the rate of females. Adding

a business qualification to existing expertise appears to be in keeping with a pathway to intra- or

inter-organisational career progression achievable by individuals who are continually in the work

force. Females tend to have an intermittent pattern of labour force participation, and this

discontinuity has a negative effect on the pursuit of intra-organisation mobility, but increases their

level of inter-organisational mobility (Valcour & Tolbert, 2003).

The proportion of females adding a business qualification in the current results is similar to

the 30% female component of US MBA enrolments in prestigious schools. Recent figures from the

University of Western Australia show that the number of females in MBA degrees has increased

over the last 25 years, from 3% to 45 to 50% (Still & Souter, 2005). Despite the equity in numbers,

Still and Souter’s findings suggest that the outcomes of MBA study in level of employment and

remuneration strongly favour males. This claim is in keeping with other findings (Mallon & Cohen,

2001; Reynolds et al., 2007) of a perceived bias favouring males, referred to by Mallon as “the

gendered processes in organisations” (2001, p. 228). Such a perception could perhaps mitigate

against the enthusiasm of women for adding a business qualification for promotion into

management.

8.6.2 Social norm expectations and gender-based differences in outcome. As most

participants were childless at the time of returning to study, parenting issues did not impact on their

decision to return to study. However, in the case of Keith described above, career outcomes for a

male were compromised by parenthood. This example partly demonstrates the findings of Reynolds

et al. (2007) that marriage, and in this case parenthood, lowers the odds of achieving occupational

expectations. However, they note that the effect is particularly potent for women.

Two female participants had accepted that they would adjust the employment outcomes of

their disjunctive change in response to any relocation requirements of their partners’ occupations,

even though that may severely limit their own employment options and opportunities for career

development. For example, their husbands could be relocated to a small country town where

opportunities for professional development in business would be very limited. “This possible

propensity of women to make sacrifices in their careers for the sake of their husbands” was raised

by Valcour and Tolbert (2003, p. 771), again indicating an acceptance of traditional family roles.

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Though both genders are subject to sexual harassment at work, the frequency is much higher

for women (Cogin & Fish, 2009). Women’s vulnerability to such harassment in the workplace is

well documented (Cesario, 2013; Cogin & Fish, 2009; Nelson & Burke, 2000; Srivastava, 2004;

Yousaf, 2014), and contributed to the decision to change occupation for two of the female

participants.

In summary, from the gender perspective, the present results suggest that the occupational

and career decisions and outcomes of both males and females are strongly influenced by social and

family-role expectation and resulting responsibilities (McMahon, 2015). Despite anti-discrimination

laws, many studies as listed above have noted that occupational outcomes of second study in the

business field strongly favour males.

8.7. The Interrelationships Between Financial Issues and Changing Broadfield

In keeping with previous findings all participants stressed that financial survival was a major

consideration in their change decisions (Carless & Arnup, 2011; Neapolitan, 1980; Robbins, 1978).

Neapolitan found that financial issues were the main obstacle deterring or delaying occupational

change. Participants weighed up their financial options before selecting their change qualification

including (for some), the availability of employment in the new broadfield, the level of degree (e.g.,

masters degrees were usually shorter but more expensive), mode of study (e.g., part-time or distance

study would allow more time to work but would take longer to complete), and funding of living

expenses if studying full-time. None of the participants were totally financially supported by

partners or family during their return to university, which as Robbins noted makes change more

difficult.

Eight participants had started (or would start) on entry-level salaries in their second

professions, and had a student loan debt from their change qualification. At interview, seven were

still paying off their debt, including three who were faced with paying off large accumulated student

loan debts from both of their qualifications. The three who had studied their change qualification

part-time were not as financially disadvantaged, but were very concerned about their level of debt.

Fay (Teacher) and Kate (Veterinarian) were particularly concerned about their financial

situations. They had not been able to find full-time employment in degree-related occupations on

initial graduation, and were on entry-level salaries in their new professions. Of necessity, both had

worked in various lower-level positions before returning to study, and had had to study their second

qualification full-time (5 years for Kate), with income from part-time, semi-skilled type work, and

government benefits. Since their initial graduation they had had little opportunity to save, and

because of her dire financial situation, Kate was unable to complete specialist training.

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Typically, young people accumulate human capital via continually increasing expertise in

their initial occupation (Bynner, 2005) to establish financial security in their 20s (Arnett, 2000). In

contrast, most of the current participants surrendered much human capital, lost considerable

income, accumulated study debts, and started again on new-graduate salaries when nearing age 30

or older. It is generally accepted that the 20s is a time of job mobility (or more rarely, occupational

mobility). However, as discussed earlier, many of the present participants who studied full-time for

disjunctive change, rather than building expertise in one occupation, were not receiving the

lucrative remuneration from their initial higher education to fund their continued learning. Thus it

appears that Bynner (2005), Arnett (2000), and Goyder (2014) may have assumed that the extended

study they described was add-skilling or up-skilling, as they did not mention the opportunity costs

of disjunctive change that many current participants experienced.

In his comparison of outcomes from liberal arts degrees and applied programs, Goyder

(2014, pp. 35-36) assumed that income of university graduates should increase with age because of

“job experience, seniority and progress through the ranks”. Such income increases would apply in

only a very limited way to students undertaking disjunctive change. As some participants

experienced, disjunctive changers effectively start as new graduates in their change-occupation.

Thus funding a disjunctive change is potentially the major practical issue in such a change decision,

and one that has received little attention in the literature.

The issues of study costs and modes of study tend to interact in their effect on change

decisions, as already discussed. The quantitative data revealed some notable patterns. Respondents

in the middle-range age groups (i.e., 30 to 49), who were possibly up-skilling, tended to select

shorter but more expensive postgraduate study such as MBA programs. Males tended to choose

part-time study more than females, but this may have been related to the modes of offering of their

preferred change choices: business for males, and education and health for females. Qualifications

in education and health tend to be offered full-time.

8.7.1 Financial characteristics of particular professions that affect their appeal as

change qualifications. Given the dominant influence of financial issues in the viability of

disjunctive change, the attraction and viability of occupations as second-study change options may

depend on whether or not the expected remuneration outcomes would enable the changer to recoup

the opportunity costs of the change in a reasonable time frame. Relatedly, the older the changer, the

higher the opportunity cost of the change and the shorter the time in the workforce to recoup that

cost (Shniper, 2005).

Some professions require many years for full registration including qualification (e.g.,

psychology), but may not promise sufficient returns for the change to be financially viable. From

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Keith’s experience and Brian’s investigations in medicine, the financial rewards make the change

financially possible. In comparison, though Kate was able to find regular employment as a

veterinarian, she felt it would take a long time to recoup her costs, given the relatively poor level of

remuneration and the expenses involved in living in the remote locations where positions were

offered. The availability of the shorter professional Masters degrees, which are offered for full-time,

part-time, and external study, can increase the feasibility of change.

8.8. Participants’ change outcomes

Except for the oldest interview participant, all those who had completed their change

qualifications were in full-time employment, utilising that qualification either alone or in

conjunction with previous expertise. This result supports Chesters’ (2014) findings of good

outcomes for mature graduates in Australia. Late entry, however, can limit career development in

some professions because of financial considerations and the life-stage correlates of age, as

described earlier with Keith in medicine and Kate in veterinary science. Both had to discontinue

specialist training. There are suggestions of ageism in the literature (Purcell et al., 2007), with

mature-age new graduates in financial industries (e.g., banking); this issue may have influenced the

choice of public service employment by two participants with second study in business. Law firms

also appeared reluctant to employ a female mature-age new graduate as discussed. At interview,

two participants were still completing their change qualification.

8.8.1 Were the Outcomes Disjunctive? Despite the initial intent of disjunctive change, at

least two participants who had change qualifications in business found they were able to combine

considerable specific-skill expertise from both qualifications in their workplace. Thus, given the

level of skills transferability, they rated their changes as add-skilling, which is in keeping with the

generally perceived compatibility of business with many other fields for career progression.

Other participants saw minor links (in skills and knowledge) between their two degrees,

though they still considered their change to be disjunctive because of substantial differences in

skills and knowledge sets, and the nature of the professional practice. The participant who changed

from engineering to business and obtained administrative positions in the public service, accurately

considered his change as re-skilling for disjunctive change rather than add-skilling. His specific

engineering skills were irrelevant in his position in the public service and he had made no attempt to

find employment as manager in an engineering firm.

Thus intended disjunctive change can actually be add-skilling, and the contention of Markey

and Parks II (1989) that true disjunctive change is relatively uncommon is supported by findings in

both phases of this study. In the event of disjunctive change, late entry can place limitations on

career development in professions for reasons of cost, time, life-stage demands, and possible

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prejudice in some private industries. In the next section, the results are considered in relation to

models of career development and the mobility discourse.

8.9 Low Skills-and-knowledge-based Mobility: Implications for Mobility Models

The present quantitative results suggest low levels of skills-and-knowledge-based

mobility in knowledge occupations, and various types of difficulties (qualitative phase) involved in

changing between such occupations. These issues raise questions about the application of theories

of career development and mobility models of career, to knowledge occupations. Similarly, Briscoe

and Hall (2006a) have acknowledged that the mobility models have reduced application when

mobility options are limited.

Clearly, from this research, several factors may tend to limit mobility in knowledge

occupations to traditional promotion (traditional model), movement within or between organisations

and changes in modes of practice, for example from being an employee to private practice. Firstly,

many knowledge occupations are professions bounded by specific qualifications mandated by

government legislation, which restricts ease of entry to such occupations. Secondly, the opportunity

cost of disjunctive change between specific-skill knowledge occupations was found to be very high,

and the change process socially disruptive and time-consuming. Relatedly, there were sharp

reductions in numbers of changers in the older age groups, possibly because of the higher

opportunity cost of changing occupation (linked to human capital accumulated over years of

practice), and the shorter the time to recoup that cost.

It is difficult to reconcile these findings with some statistically-based predictions of large

increases in lifelong job and occupational mobility in future ‘new careers’ in an economy with

increasing numbers of knowledge workers (e.g., Jarvis & Keeley, 2003; McCrindle, 2014). Rather

than disjunctive-type mobility, transitions in knowledge occupations are more likely to involve

changes in employer; that is, job changes, changes in mode of practice, for example to private

practice, or changes to skills-related occupations achieved by gradual adaptation.

Attention to terminology is particularly important to avoid creating a misleading impression

of unlimited mobility options generally in the world of work. With some add-skilling, for example

of management skills, individual professionals could move into supervision or administration

within their profession. Specialisation or private practice would also be a change option. In both

cases, professionals would be building on expertise in their profession in different ways, but

remaining within the same profession. Transitions to management may be pathways to future

adaptions: for example, to administration in a related profession. Thus the boundaryless model as a

model of self-managed specialisation and add-skilling for gradual transition, rather than

occupational mobility, could apply to bounded knowledge occupations. Similarly, knowledge

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occupations could be considered protean careers if the ongoing professional development

mandatory in many such occupations could constitute the ongoing incremental learning. However

it is important to emphasise that the flexibility for ongoing skills-and-knowledge-based

occupational change at level throughout a lifelong career, which is sometimes implied by advocates

of the protean career concept (e.g., Hall & Mirvis, 1995), is unlikely to be available to individuals

in knowledge occupations.

In summary, these results suggest a questioning of the mobility discourse, and strengthen

the doubts expressed by some contributors (e.g., Feldman & Ng, 2007; Inkson et al., 2010; Kim,

2013; Richardson, 2012; Rodrigues et al., 2015; Rodrigues & Guest, 2010; Roper, Ganesh, &

Inkson, 2010) about the generalisability of the mobility models of career and the functioning of the

proposed new careers/knowledge economy of the future. The present findings imply that

individuals have a preference for the job security of a specific skill occupation, and further, that

knowledge occupations could be considered a distinct occupational group to which applicability of

the current generalised mobility and self-management discourses is reduced by the limited

availability of occupational change.

8.10. Summary of the Discussion

Addressing the question of mobility in knowledge occupations, the quantitative findings

suggest that the further credentialing of university graduates was primarily up-skilling in their field

of expertise. With those who studied in a different field, age-related patterns of change suggested an

intent to add-skill for career advancement in their field of expertise (e.g., engineer to manager of

engineering), rather than re-skill for disjunctive occupational change, particularly for those aged 30

to 39. Also from the patterns of change (e.g., generic courses to medicine and education in the

under 30 group), some returnees who had changed their field of study may have been completing a

pre-planned programme. Thus within the limitations of the secondary data set, the outcomes support

previous findings (Markey & Parks II, 1989; Roksa & Levey, 2010) that mobility in knowledge

occupations was very low, in terms of actual skills and knowledge set change.

The results also indicate that the most popular second-study choices were business and

education, and the least selected choices were STEM Fields. The broad-field changes were

predominantly from generic type qualifications, which have less clear or less defined initial labour

market outcomes, to occupationally specific degrees. This pattern of change suggests a preference

for job security and stability with possibly flatter career trajectories, over more risky and personally

challenging career structures with the potential for eventually higher remuneration if well self-

managed.

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Contextual variables were also found to influence study patterns. Levels and types of

mobility and patterns of broad-field change varied with age. From the gender perspective, the

present results suggest that the occupational and career directional decisions and outcomes of both

males and females were strongly influenced by social and family-role expectations and

responsibilities. All participants stressed that financial survival was a major consideration in their

change decisions, in keeping with previous findings.

Phase 2 interview data provided insights into the antecedents of, and motivations for,

individuals’ returning to study to change broadfield. Change decisions were generally related to

dissatisfaction with the employment outcomes from their initial qualifications, and partially the

result of poor initial degree choices, with little investigation of outcome options. Change choices

were made with the intention of avoiding the reasons for their earlier dissatisfaction. Major

financial, gender, and age-related life-stage issues and difficulties reportedly weighed on the

decision to change, and on the process and outcomes of the intended disjunctive changes.

Satisfaction with the undertaking was related to the employability of the mature-age new

graduate in the second-choice occupation, and on whether the remuneration levels would enable the

changer to recoup the opportunity cost of the change. The disadvantages of late entry to particular

professions were related to life-stage and financial constraints, but also to possible prejudices in

industry. At interview, two participants were disappointed at their outcomes.

Low levels of disjunctive change between knowledge occupations highlight the importance

of the initial broadfield choice in these high-initial-investment, higher-education occupations, which

has implications for career guidance practice. These results suggest that the generalisability of the

mobility models of career to knowledge occupations warrants reconsideration, especially with

respect to the current mobility discourse and the envisaged ‘new’ very mobile careers in the

knowledge economy. In Chapter 9, the theoretical contributions of the study and the implications

for policy are presented.

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Chapter 9

Project Summary, Conclusions, and Implications

Mobility, in the work context, is a process common to many theories of career, and features

prominently in the discourses on the nature of the future world of work. However, there appear to

be some inconsistencies in the mobility-related predictions of the nature of ‘new careers’ in the

future ‘knowledge economy’. Greatly increased demand for university-qualified ‘knowledge

workers’ who will be both increasingly mobile and increasingly specialised is forecast, when highly

educated workers are thought by many (Kambourov & Manovskii, 2008; Sicherman & Galor, 1990;

Super, 1980b; Sweet, 2011) to have very low occupational mobility.

Arguably these apparent theoretical inconsistencies may have developed partly because the

term, mobility, is used generically in the multidisciplinary field of career studies, to refer to many

types of change in employment situations, from changes in how work is delivered, to physical

relocation, promotions, or actual occupational change (Feldman & Ng, 2007). The boundaryless

model of career development (Arthur, 1994; Arthur & Rousseau, 1996), and the protean career

(Hall & Mirvis, 1995) were proposed from the management perspective of the authors in the

context of a narrative of rapid technological, organisational, and social change. These models have

strongly contributed to the increasing focus on mobility in the literature since 1994 (Pringle &

Mallon, 2003; Feldman & Ng, 2007)). However, little attention has been paid across career-related

disciplines to distinguishing between different types of mobility. Further, the forecast of rapid but

undifferentiated increases in mobility has been propagated in the media. This dominant mobility

discourse (Inkson et al., 2009) may be misleading and unsettling for young people choosing their

career direction, especially for university aspirants contemplating financial investment in a

particular field of study.

To investigate these theoretical inconsistencies in future predictions of increased mobility

and increased specialisation in knowledge occupations, the current research focused on the nature of

mobility in relation to these higher level occupations, where entry is often gatekeeper-protected and

the availability of mobility purportedly low. It is acknowledged that individuals in these

occupations may self-manage a change between organisations and employers with relative ease, or

from employed practice to contracting, private practice or relocation, all of which are forms of

mobility. However, crossing occupational boundaries and actually changing to another knowledge

occupation requiring a different skill-set (i.e., skills-and-knowledge-based mobility) has received

only limited attention. Further, the ease with which such skills-and-knowledge-based mobility can

be achieved determines the importance of the initial field choice in constraining lifelong career

options. Moving on from generalised discussions of mobility, this research aimed firstly to

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examine the fields of study of initial and recent qualifications as indicators of the possible future

occupational intent of individuals who undertook further credentialing, and thus to gauge the level

and nature of perceived mobility options. Secondly, the study explored motivations for undertaking

occupational transition and disjunctive occupational change, the factors involved in undertaking

such changes, and the expected outcomes.

This final chapter considers the results discussed in Chapter 8 in relation to research

outcomes, the contributions to career theory development and practice, lifelong learning theory,

research methodology, and policy. Limitations of this research and suggestions for further research

are then discussed and the conclusions presented.

9.1 Outcomes of the Research

Extra credentialing in individuals already with degree-level qualifications was evident from

the quantitative data. Examination of fields of initial and later study revealed that significantly less

than half the returnees had changed their broadfield of study, and that there were large differences

between fields of study in popularity as second study choices with business and education being the

most frequently selected. Patterns of study suggested that the intent of some of the field changes

was more likely to be add-skilling for advancement in an established occupation (e.g., from

engineer to manager of engineers), than re-skilling for disjunctive occupational change (e.g., from

engineer to social worker). Subtracting these probable ‘add-skillers’ and a number undertaking

likely pre-planned degree sequences (e.g., Bachelor of Arts/Diploma of Education), the level of

intended disjunctive change between knowledge occupations appeared to be very low. Human

capital theory suggests that individuals’ mobility options are limited by the opportunity cost of

disjunctive change, while ageism is also prevalent in some industries (Blau et al 2009; Robst,

2008). Therefore, some initial career decisions may be effectively irreversible at level (Ginzberg,

1951; Johnson & Mortimer, 2002; Krumboltz, 1979; Scott, 2007; Super, 1957).

With those re-skilling, the dominant direction of broadfield change from generic to

occupationally-specific degrees suggested the importance of job outcomes, particularly job security

(Baruch et al., 2015), as a motivation for change (Carless & Arnup, 2011). Changes undertaken

were significantly related to particular broadfields of initial and recent qualifications, age and

gender, with change patterns mostly in agreement with previous findings as discussed in Chapter 8.

Some findings from the qualitative phase also support previous opinions that disjunctive

change or ‘classic transition’ (Mayrhofer et al., 2005) in knowledge occupations can be a

destabilising, expensive and stressful undertaking (Blau 2007), which renders the likelihood of such

a change uncommon.

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Thus the results combine to suggest that the many costs of actual disjunctive change

between knowledge occupations render the undertaking highly unlikely as a mechanism of even one

occupational change and certainly as a lifelong mechanism for career development. Instead,

individuals would need to look within their field of expertise for avenues of change via, perhaps

adaptation or transition. Therefore, the initial choice of speciality could largely determine their

future career pathway options. Thus the positive promise of lifelong control over career

management for knowledge workers could well be very misleading when in fact the limitations on

mobility of the highly educated are what Baruch and Vardi (2016) described as the “dark side of

contemporary careers” (p. 355) and are of prime importance in initial career planning.

9.2 Contributions of the Research

The contributions of the research to career scholarship stems from the focus away from

specific theory to in-depth consideration of a fundamental element of most career theorising:

mobility. The process of mobility features so prominently in career thinking that it has developed

into part of a dominant discourse (Inkson, 2009; Richardson, 2012). Yet surprisingly little attention

has been given to the examination of the varying natures of mobility, which has contributed to

vagueness in theory construction. Following their discussion on mobility, Feldman and Ng (2007)

concluded that “ it is critical that researchers start taking a finer grained look at career mobility (p.

369), and that “lumping all kinds of mobility together” (p. 369) can result in overestimations of the

degree of mobility in the population. In this section, the contributions of this mobility-focussed

research to methodology, career theory, lifelong learning theory, and the implications for practice

are discussed.

9.2.1 Methodological contributions. Methodologically, this research contributed a new

approach to identifying different types of mobility in higher level occupations, and demonstrated

the value of a cross-disciplinary approach and definitional rigor in career issues.

Previous studies examining rates of mobility used self-reported job/occupation titles in

conjunction with various occupational-classification scales to distinguish occupations and locate

occupational change, but such research has various limitations as discussed in chapter 1

(Kambourov & Manovskii, 2008; Parrado et al., 2007; Perales, 2014). Names of occupations are

inconsistent indicators of requisite skills-and-knowledge sets, and so poor measures of actual skills-

and-knowledge set changes as Parrado et al. (2014) reported. Detecting such changes is essential in

assessing occupational as opposed to job change. To overcome problems from the plethora of

occupation names, and the inconsistencies of self-reports, the present research examined fields of

qualification. For each individual, the names of initial and later university qualifications were

compared, using a scale of educational qualifications, the ASCED, to classify graduates’

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qualifications from a quantitative data set. The fields of study were indicative of particular skills-

and-knowledge-sets, and any change between a graduate’s initial and later qualifications could be

accurately identified. By including graduate study patterns in all relevant fields in the ASCED, the

quantitative results produced a comprehensive, real-world picture of comparative second-study

preferences, and ‘stay or change’ patterns within and between all fields, in association with

demographic and contextual factors. This allowed better understanding of the type of career

structures and pathways considered viable by individuals with university qualifications. Further,

the improved accuracy achieved by using changes in skills and knowledge as an indication of

mobility provided a way of separating occupational mobility from other types of mobility, e.g., job

or geographical mobility, so that the approach has contributed an important empirical process to

career research. The apparent assumption here of the relationship between fields of qualification

and occupation or occupational intent has been discussed in Chapter 3 and in Limitations later in

this Chapter.

Examining mobility options from a multidisciplinary perspective, supported by definitional

rigor throughout, generated an in-depth recognition of factors affecting mobility options in

knowledge occupations. By using theoretical formulations from several disciplines, the present

study contributed a step towards remedying the siloing in career studies, recommended by many

(Arthur, 2008; Gubler et al., 2014).

9.2.2 Contributions to career theory. Rather than focus on a specific theory, this thesis

focused on mobility, a process common to many career theories and discourses about future careers,

and contributed new knowledge to career theory in several main areas, specifically: the importance

of differentiating types of mobility in career theory and research; the limitations on mobility in

knowledge occupations; the preference for job security over mobility in knowledge occupation; the

importance of adding an objective focus in career research; and the possible overemphasis on the

theoretical modelling approach to career. These theoretical contributions are discussed in turn.

The importance of differentiating types of mobility in career theory and research. This

research drew attention to the arbitrary way the term mobility is incorporated without differentiation

in career scholarship which has arguably resulted in misleading predictions about mobility in future

careers. The methodological approach in this study as described in 9.2.1 enabled a movement away

from measuring mobility as a generic concept by delineating different types of mobility. When

occupational mobility intent (re-skilling) was separated from other types of mobility for career

development, such as up-skilling or add-skilling, the apparent level of actual occupational mobility

intent in the quantitative study was quite low, as predicted by Feldman and Ng (2007). Failure to

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distinguish types of mobility in career theory has resulted in a misleading impression about the

types and levels of changes actually undertaken in the careers of individuals and in the world of

work. It follows that the dominance of the mobility models in the career literature developed partly

because mobility was treated as a generic concept. A central problem in career theory development

is that the nature, process, practicability, or limitations of mobility are rarely if ever considered or

defined. Thus this research has demonstrated the importance of differentiating types of mobility in

obtaining reality-based information on which to base theoretical constructs.

The limitations on mobility in knowledge occupations. With an in-depth, cross-discipline

analysis of the factors affecting mobility in knowledge occupations and an examination of the

patterns of broadfield change, the present study has demonstrated that ongoing unlimited mobility is

extremely difficult to achieve in higher level occupations, unless the mobility is predominantly

changing jobs or changing to a closely related occupation, e.g. from engineer to manager of

engineers.. A similar caveat would be required to avoid a conflict regarding the prediction of

simultaneous increases in both mobility and specialisation in knowledge occupations. From human

capital theory (Campbell & Banerjee, 2013; Frank & Walters, 2012) as discussed in Chapters 2 and

8, the higher the level of expertise, the more difficult it is to undertake disjunctive change. Thus

empirically, the quantitative findings have indicated an imperative for re-examination and

reformulation of predictions on the nature of mobility in future careers in knowledge occupations.

The preference for job security over mobility in knowledge occupations. As in the study by

Jackson and Wilton, (2016), proponents of the future new career forecasts see increased mobility as

desirable, and aim to develop congruent attitudes in graduates. However, in the present research,

disjunctive broadfield changes were predominantly from general to more occupationally specific

degrees, which have been related to easier initial labour market entry and job stability, but lower

career trajectories. This change direction suggested that graduates seeking to enter the workforce in

knowledge occupations value job security over mobility. In this way, the present findings suggest

that graduate employment preferences are at odds with the mobility narrative in the ‘new careers’

literature, which has implications for career practice as discussed below.

The importance of adding an objective focus in career research. Extensive research over

decades has focused on aspects of career from the subjective perspective of the individual

(Lazarova & Taylor, 2009), such as psychological mobility (Sullivan & Arthur, 2006), career

orientation (Gerber et al., 2009; Herrmann, Hirschi, & Baruch, 2015), career attitudes (Briscoe et

al., 2006; Stoltz & Young, 2013), self-efficacy, self-regulation and positive emotions (Murtagh,

Lopes, & Lyons, 2011); but this is only one side of the discipline of career. There has been some

neglect of practical, objective issues (Inkson, 2006) increasingly acknowledged to impede the

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freedom of individuals to design their careers lifelong (Tams & Arthur, 2010). As a result: “We

know much more about willingness to move than actual mobility itself” (Feldman & Ng, 2007, p.

370). From the relationships discovered between contextual, gender and funding issues, and

patterns of broadfield change in the quantitative study and the practical concerns raised in the

qualitative phase by all interviewees, this research demonstrated the importance of a focus on the

practical, objective realities that career decision-makers have to interact with.

Possible overemphasis on the theoretical modelling approach to career. Much career

theory, specifically that related to boundarylessness and the ‘new careers’ was largely formulated

without an empirical base (Baruch et al., 2015; Inkson et al., 2012; Ituma & Simpson, 2009;

Mayrhofer, 2012; Rodrigues & Guest, 2010), or attention to definitional rigor (Inkson et al., 2012).

The theories appear to include the process of mobility as a generic concept and do not incorporate

contextual adjustments, which limit their contribution to the discipline. The usefulness of these

career theories and models is surely related to the extent to which they represent the real world

(Baruch & Vardi, 2016; Hodkinson, 2008). Theorising without caveats implies that career

development processes and mobility opportunities apply equally to all in all situations, which is

highly unlikely. By examining mobility in one occupational group from a multidisciplinary

perspective in the context of an empirical analysis, the present study identified basic flaws in

theoretical formulations and predictions on the nature of mobility in future careers. Thus this

research has supported and extended previous findings questioning the current orthodoxy of an

increasingly mobile future workforce (Ituma & Simpson, 2009; Kattenbach, Lücke, Schlese, &

Schramm, 2011; Mayrhofer, 2012; Rodrigues & Guest, 2010); which is concerning, considering the

prominence of this formulation of the future, and the extent to which it has influenced career

intervention. The present research delivered detailed relationships between variables from which it

was apparent that incorporating the complexity of interacting and fluctuating career variables in a

general way may be extremely difficult. Thus it may be that career scholars have placed too much

emphasis on modelling career development in many and varied ways at the expense of empirical

investigation of the tasks and realities the discipline was initially created to address and is centred

around.

In summary, rather than focussing on a specific theory, the theoretical contribution of this

thesis stemmed from a focus on mobility, a fundamental element in career theory. An in-depth

analysis of mobility in one occupational group, considering the interactions between the relevant

variables, demonstrated the complexity of the task of developing models and theories of career

applicable to the real world.

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9.2.3 Contributions to lifelong learning theory. Lifelong learning is a concept of

ambiguous promise (Gibb & Walker, 2011), related to the mobility discourse in the context of the

‘new careers’ and the knowledge economy. Arguably, the concept has become a discourse in itself,

and one frequently relied on by politicians and broadcast in the media as a panacea (Nicole &

Edwards, 2004) for employment-related problems in the economy (e.g., deindustrialisation,

redundancy, advanced age).

Limitations tend to be glossed over, but the present findings highlight some of the

challenges related to the generalised application and usefulness of the concept in relation to

mobility between knowledge occupations. As current participants testified, returning to tertiary

study is very costly financially and socially, particularly when undertaking disjunctive occupational

change. Hence, repeating the process of disjunctive change as a mechanism of career directional

change lifelong would be highly unlikely. Thus formal lifelong learning for knowledge workers

would be mostly add-skilling or up-skilling in pre-existing directions to adapt to technological

changes (Greller & Simpson, 1999) or for promotions. Also, perhaps the continuing professional

development discussed by Collins et al. (2012) which has long been mandatory in most professional

occupations could be re-classified as lifelong learning.

However, as Gibb and Walker (2011) point out, lifelong learning in Canada is not directed

at producing more knowledge workers, but rather at providing short training programmes in, for

example, literacy skills for low-skilled workers. In the Australian context, Reeson et al. (2016)

discuss lifelong learning in relation to TAFE courses which can be much shorter, cheaper, less

academically demanding and more practical skill-focussed than university study. Such

qualifications at AQF levels 2, 3 or 4, could enable retraining for occupational mobility but not at

the level of knowledge occupations.

Thus lifelong learning as a concept is not new, particularly in relation to ongoing

professional development in higher level occupations, but the actual skills-and-knowledge-based

nature of the concept is vague because of poor definitional rigor, which has led to ambiguity in the

understanding and application of the concept. The present findings indicate that learning for

disjunctive occupational change in higher level occupations would not be viable as an ongoing

career development strategy, and so should not be represented in the broad term ‘lifelong learning’.

9.2.4 Contributions to practice. The implications for career practice necessarily flow from

the contributions of the findings to career theory. The main practice issues impacted are:

approaches to career intervention; the career development effects of limited mobility in knowledge

occupations and the preference for job security; and the practice response to an increased

importance of external factors. These implications will each be discussed in detail.

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Approaches to Career Intervention. A major development from the dominance of the

mobility models of career and the ‘new careers’ of the future (Jarvis et al., 2003) is that career

guidance in many western countries has moved towards “life planning rather than occupational

choice” (Savickas, 2008, p. 111), that is away from the traditional role and perhaps client

expectations of career guidance. Online career intervention programmes based on the ‘life design

approach’ are being developed for adolescents (Nota, Santilli, & Soresi, 2016). High school

students are educated in career competencies and career attitudes for lifelong career self-

management (CSM; Forrier, 2009) and for continual transition in mobile, new careers of the future

(Stoltz & Young, 2013, p. 342). This approach to career practice assumes the generalisability of the

mobility models, the accuracy of the forecast nature of the ‘new careers’, and the ready availability

of change options (Borghans & Golsteyn, 2007), all of which are seriously brought into question for

knowledge occupations by the present results.

The career development effects of limited mobility in knowledge occupations. From the

current findings, career practitioners need to be aware that protean-type career self-management has

major limitations in many fields of knowledge occupations ( King et al., 2005; Okay-Somerville &

Scholarios, 2014). Despite a protean or boundaryless mindset, individuals in specific-skill type

knowledge occupations may find their employment options at level confined to jobs and

occupations within their initial field, using the same or a closely related skills-set. Alternatively,

they could undertake disjunctive occupational change, found to be stressful, disruptive and very

costly by current participants, Clearly, then, the original choice of qualification and skills-and-

knowledge-set can be of paramount importance in determining lifelong career development

pathway options (Baird, 2012). This limitation on mobility is a pivotal matter for practitioners

working with university aspirants. Thus an essential contribution of this thesis to career practice is

the empirical establishment of the need to maximise the effectiveness of working with university

aspirants who are making their initial degree choices. The development of life-long career self-

management skills and mindsets should become adjunct, rather than prime tasks with such clients.

An integral part of assisting university aspirants is ensuring they understand the potential

limitations around mobility in particular knowledge occupations, including the issue of the

comparative outcomes of generic/occupationally specific degree choices, and the associated job

security/ mobility factors as discussed in this thesis. By employing the skills-and-knowledge-based

approach to mobility demonstrated in the present research, to a deeper level, charts could be

developed of the skills-and-knowledge linkages within and between occupations and fields, to

enable clients to better explore potential pathway options from various initial choices. These charts

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would be useful for both initial client information and for later gradual transition or adaptation

planning.

A suggested practice response to the increased importance of external factors. More

recently, with the recognition of limitations faced by individual career self-managers, (Tams &

Arthur, 2010), some studies have challenged the notion that there is a universal way of managing

careers (Gerber et al., 2009). Therefore, to provide a more comprehensive approach to career

guidance, the development of an empirically-driven, reality-based body of local and international

knowledge should be considered, that contained input from employers, but also more wide-ranging

experiential type information and realistic positive and negative issues, and obstacles. With the

current focus on teaching career self-management as detailed in the Blueprint, much of the task of

resourcing career information is placed with career clients which is quite daunting, so that it is

likely than choices will be made on less than sufficient information and with many options

unknown. From the present findings, with lifelong mobility options limited in knowledge

occupations, the provision of information on external factors is particularly relevant.

It is concerning that none of the interview participants elected to have a one-on-one

discussion with a career professional before making their initial choice, or more particularly, their

change choice. Of course it is not possible to know if a consultation would have produced better

initial outcomes and avoided their needs to change. The implication could be that either access to

career professionals is limited, or the community does not value what career counsellors offer, or

that career services are poorly marketed. Practitioners and professional bodies need to consider

these implications.

In summary, the important contributions for practice of the present findings are the

imperatives for practitioners to be aware of the likely lifelong mobility limitations for university

graduates, which may vary according to broadfields, and adjust the focus of their interventions with

high school students and university aspirants in view of this. Practitioners should also be aware of

the economic implications and social issues related to disjunctive change in knowledge occupations

and generally ensure they engage in regular professional development to keep pace with empirical

findings.

9.3 Implications for Policy

The research results have several implications for policy. These relate to: the structure of

university programme models; attracting students to STEM courses; the congruence of university

programmes and labour market demands; the limitations of lifelong learning as a concept; the

preparation of graduates for professional employment; and the provision and utilisation of career

guidance in Australia.

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The structure of university programme models. Malamud (2011) found that later

specialisation in higher education reduces the likelihood that individuals will make the costly switch

to an unrelated occupation. Consideration could be given to an extension of the generic

degree/graduate school model for occupationally-specific occupations, for example, as currently

operates in the USA and in some programmes in Australian universities.

Attracting students to STEM courses. From the quantitative findings, with the predicted

high future demand for qualified scientists and technologists in Australia, attention to issues in

STEM-related occupations is warranted to attract students and reduce attrition. The outcomes for

individuals, who are arguably very able students (Arcidiacono, 2004) in very difficult courses

(Frank & Walters, 2012; Reimer et al., 2008) are currently not encouraging. Despite the knowledge

economy rhetoric, Australia is commonly described as post-industrial because industries are

shutting down, mining is reducing, and there are limited degree-related employment opportunities

for mathematics and science graduates. In view of the apparent losses in participation in STEM

fields, the current push to encourage students into STEM courses is understandable but

unreasonable, if outcomes remain limited, as research suggests. Encouraging able students to study

science and maths up to year 12 would keep their future STEM options open. Also, if the generic

value of a STEM qualification were marketed to employers, graduates would be sought after for

their general ability levels, as they are in USA and Europe (Reimer et al., 2008). This could raise

the general profile of a STEM education, improve outcomes, attract students, and thus maintain

some general expertise in STEM fields. The implications for relevant policy makers such as

governments and educational institutions of the high levels of attrition in STEM courses found in

the current study and the little attraction STEM has as a second study, is that a detailed vision of the

variety of outcomes for STEM graduates should be created and marketed. If attention is not paid to

these findings, expertise in some STEM fields in Australia could decrease to a level from which it

would be very difficult to recover because of a papacy of sufficiently qualified leaders and teachers.

Experts would need to be imported.

The congruence of university programmes and labour market demands. The patterns of

change from general to occupationally specific fields in the quantitative findings suggest support for

the contention that mismatches between university courses and labour markets is an issue of

concern for individuals and the economy (Hemsley-Brown & Foskett, 1999), and therefore

something policy makers should seriously consider.

The limitations of lifelong learning as a concept. As discussed in relation to the

quantitative results, it is important to revisit the realistic, skilled-based nature of lifelong learning,

and the limitations of the concept as a process of retraining for occupational change at all levels and

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all ages. Otherwise, unwarranted assumptions about its efficacy could become embedded in public

policy. For example, with proposed changes in Australia’s aged-pension legislation, it has been

suggested that older people be retrained for occupational change to ensure they remain in the

workforce (Griffiths, 2014). The present study suggests that lifelong learning is likely to consist

mostly of add-skilling or up-skilling, but little re-skilling in knowledge level occupations.

The preparation of graduates for professional employment. From the comments of many

participants, universities and professional associations need to discuss their respective roles in

preparing students in occupationally-specific qualifications for the emotional demands of

professional practice. Including personal development units in courses and/or introducing a mentor

system (possibly via the relevant professional association) may improve students’ early professional

experiences and thus minimise the need for disjunctive career change.

The provision and utilisation of career guidance in Australia. The availability and nature

of career guidance for school leavers, university students and the general community in Australia is

unclear, as is the extent of utilisation of such services. Given the present quantitative and

qualitative results, government policy makers in consultation with the career industry should

investigate these important issues and make appropriate adjustments where efficacious.

9.4 Limitations of the Study

The project examined changes in skills-and-knowledge sets instead of self-reported changes

in occupations as indicators of occupational (not job) mobility in knowledge occupations. An

assumption was made that the fields of study undertaken were indicators of the occupational

intentions of graduates. As a majority of returnees undertook second study in the same field as an

earlier qualification (up-skilling), or in a field in keeping with a recognised career progression

pathway (add-skilling), and as most change was from generic to specific fields where employment

is usually easier to obtain, the assumption would seem to have validity. Robst (2006) found that

graduates from occupationally specific degrees who did not work in the field of their study major,

were disadvantaged in terms of income.

There were several limitations resulting from the use of a secondary data set that was not

purpose-designed for this study. The important question about the name of previous qualifications,

which was required for this research, was only originally included to confirm the accuracy of the

response to a related question, and the answer to this question was not submitted by the universities

who processed their own data, so that a number of respondents had to be excluded from the study.

Also, although the response rate to the questionnaire was 65%, it was not possible to ascertain

whether response rates of particular categories reflected the makeup of the whole graduate cohort,

` 217

e.g., age groups, gender. Purpose-constructed research would overcome this source of potential

bias. However, potential bias related to overrepresentation by some university groups in the sample

was negated as it was established that all groups of universities were represented in proportion to

their share of the total graduate cohort.

Because the questionnaire asked for the name of the previous qualifications (rather than the

major), many respondents did not specify the major in generic-type degrees, or in higher-research

degrees, so those records also had to be eliminated. Similarly, with those who had completed

double degrees, their intended major was ambiguous, resulting in elimination of those records.

Elimination of doubtful records increased the accuracy of identification of stay or change with

respect to broadfields and patterns of study. Further research with a specially created questionnaire

and thus database would overcome this issue.

The present data set provided information only for the two available tertiary qualifications

for each respondent. Knowing the date of all the tertiary qualifications, and the order of the awards,

would have given a more accurate picture of the fields of all successive skill accumulation and

career structures. Details of employment history, and reasons for undertaking further study, would

have been useful information, but were not included in the questionnaire. It would also have been

useful to have had information on respondents’ age at completion of initial degree and

commencement of the later qualification, to gauge the number of respondents who were possibly

following pre-planned programmes. However, accurate data on such programmes would not be

possible without the ability to ask respondents their initial intentions, which suggests an avenue of

further research.

Interview participants for phase 2 (the qualitative study), were to be selected according to

the results of phase 1, the quantitative study. Specifically, this was to be done with respect to field

of study, age and gender. However, finding individuals who met the selection criteria for interview

(two qualifications at university level in different fields and separated by time in the workforce) was

particularly difficult, as they were not an identifiable group, and only nine were located in the time

available. On the other hand, the sample was matched with the quantitative data base for gender

and age, and each of the three groups of broad fields, as in chapter 4, was represented. Though the

information obtained in the qualitative phase was rich in content and largely consistent across

several important themes, it is not generalisable because of the small numbers. Nevertheless, the

interviews raised important contextual issues related to changing between high initial-investment

occupations, suggesting avenues for further research.

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9.5 Suggestions for Further Research

Despite forecasts and statistically-derived future levels of job and occupational mobility and

change (e.g., Jarvis & Keeley, 2003; Mc Crindle, 2014), the real level and nature of future mobility

patterns and career structures remains highly controversial. Future research may help clarify the

situation. First, as suggested earlier, the issues around mobility in other groups of occupations and

industries requires in-depth investigation, both in ‘Anglo-Saxon’ and other cultures. Future

research using a large qualitative study could be undertaken to explore the effects of context on

mobility in knowledge occupations. Such research should create a more accurate picture of real

situations, as a basis for the development of evidence-based theoretical models of mobility.

Second, ongoing empirical research on trends in various types of mobility generally, and in specific

occupational groups, may better inform practice.

Third, following on from the findings of the small, exploratory qualitative study, an

investigation of career outcomes in terms of career success following actual disjunctive change in

knowledge occupations in various cultures would increase understanding of the availability of

mobility for that specific occupational group. Fourth, other issues warranting further investigation

concern the nature of the generally accepted high mobility in the 20s to 30s age groups, with respect

to knowledge occupations. Relatedly, the relationship between age and type of mobility or fields of

change also requires clarification because of the definite patterns of broadfield change preference

chosen by older individuals in the present research.

Fifth, while these patterns of broadfield change preference suggested ongoing empirical

real-world investigations would shift the focus somewhat from the rather dominant psychological

focus of career research, future research should also extend to the other half of the career story:

occupations and the world of work. In this way, research would provide a knowledge base for

intervention as discussed in earlier sections. For example, as suggested earlier, research leading to

the creation of charts of skills and knowledge-based linkages between and within, jobs, occupations

and fields which would enable individuals to explore potential pathway options from various initial

choices would be a useful tool to aid career decision-making. Also mentioned earlier was narrative

research into the development of folios of ‘occupation stories’ so that clients could be provided with

in-depth, multidimensional information well beyond the level of the descriptions of jobs and

occupations currently on offer.

Sixth, patterns of utilisation of career guidance counsellors by students, both in high schools

and in other educational institutions, also require investigation. In such research, an important

added component would be to assess perceptions of the profession and whether or not it is seen as

able to meet the needs of prospective clients. Some research has been done on this in Australia at

` 219

university level (Walck & Hensby, 2003) but further research may suggest appropriate changes in

intervention.

Finally, a useful research project could involve contributors from relevant disciplines

working towards agreement on career-relevant terminology such as mobility, occupations, career as

suggested by many. Such a resource would give career researchers and theorists a communal base

of understanding from which to share their ideas.

9.6. Conclusions

This study demonstrated the usefulness of a multidisciplinary approach employing

definitional rigor in an explorative empirical study of the concept of mobility which is common to

most theories of career yet generally assumed rather than defined. The particular focus was on

mobility in knowledge occupations and the findings demonstrated that disjunctive skills-and-

knowledge-set change in this occupational group is disruptive, expensive and uncommon. Hence,

as the rate of such mobility is relatively low, the initial choice of field may be deterministic of

future career options. The predominant pattern of field change, from generic to occupationally-

specific qualifications which ease workforce entry, suggests the prime importance of job security to

changers.

The fields of further credentialing were related to age and gender, with the rate of return to

study highest for those under 30 years, and declining sharply with age. The life-stage correlates of

age also impacted change decisions and outcomes, with financial issues of principal concern. Males

and females differed in their fields of second study choices, in that men showed a preference for

business and women for education. In keeping with previous findings, the most popular fields for

further credentialing were education and business, whereas STEM fields had the highest nett loss of

participation.

Therefore, the current mobility discourse has limited application to knowledge occupations

with respect to skills-and-knowledge-based mobility and change, because the opportunity cost of

crossing the skills and knowledge boundaries between such occupations is very high and increases

with age. In knowledge occupations, change patterns suggested some transitions to related

occupations via add-skilling. The results also indicated that university aspirants should consider

their initial field choice carefully, as it may determine their future mobility options at level.

Overall, the study advanced recent research that has questioned the current orthodoxy of a future

characterised by very mobile ‘new careers’ in knowledge occupations within a knowledge

economy.

` 220

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Appendices

Appendix 1. Semi-structured Interview Schedule

Thank you very much for participating in my project. I would like to outline the process that

we will follow in the interview. You’ve given me some information about your qualifications and

work experience so we’ll start by going through that for clarification, and to add a few details such

as your age and marital status. Then we’ll look in more detail at the process of your career

development to date, mostly following a chronological order, covering things such as what you did,

and what was going on for you at the time. Do you have any questions?

Could you go through the qualifications that you have please? ( Follow up inquiries to

clarify the time line)

I need some details such as your age, and your marital/family status at the time you were

studying for both your qualifications.

What fee payment method did you choose for each qualification? Were they self-funded/

family funded/ employer funded. ( If necessary, follow up inquiries to find out what if any financial

assistance they had).

To get a picture of your residential circumstances, could you go through your living

arrangements from when you entered university? (Follow up clarification where necessary).

What subjects did you study in year 12?

How did you make the decision to do (.name of field of first degree)? (Follow up queries on

reasons for choice if not volunteered, any work experience, company visits).

Did you discuss the decision with others? (Follow up queries if necessary on whom, the

nature of their responses and advice and whether or not it was helpful).

(If not mentioned, ask about career guidance) : Did you talk to a guidance officer or career

advisor at all. If yes, ask about the experience?

When you decided to do (initial degree), what outcomes were you expecting at that point?

Exploring university course experience in initial degree

What did you think of the (name of course)?

How did you find study at university?

Exploring actions after graduation.

What did you do after you graduated? ( If answer was about finding employment, follow up

questions on their employment finding process, the nature of the position found, and what they felt

about the work and work situation. If unable to find the kind of position they were seeking, ask

` 280

more about what happened and what they did). (If answer was other

than seeking work, explore what they did, the reasons and motivations).

Exploring what led to their undertaking a change. (Their reasons for this may have

mentioned in the previous question, if so just follow up from where they start).

Ok, so now I’d like to hear about why you decided to undertake further study to change to a

different occupation. ( Follow up questions to elicit facts and reactions which could be very

different for each participant).

Exploring the process of change

Same questions as above: How did you make the decision to do (name of field of second

degree)? ( Follow up queries on reasons for choice if not volunteered).

Did you discuss the decision with others?( Follow up queries if necessary on whom. Also if

not mentioned, ask: Did you talk to a guidance officer or career advisor at all. If yes, ask about the

experience).

What was the attitude of your family and friends to your decision to return to study and to

do so in a different field?

What outcomes were you expecting from your second qualification?

What issues did you consider in selecting a particular course?

Could you please talk about any other practical issues, you may have considered in making

the decision: to return to study?

: to change occupation?

( Follow up questions to explore any issues mentioned).

Second experience of university

Could you tell me about the course experience? How did it compare with your previous

experience of university study?

Did you see that the skills and knowledge you got in (initial degree) in any way related to

what you studied in your second qualification? If so, how?

Exploring outcomes from second qualification.

What did you do after you completed your second qualification? (Follow up questions to

clarify issues, particular about employment).

Overall, now, how do you feel about making/having made the change? Any particular

positives or negatives you’d like to mention?

Are there any other comments you’d like to make about what’s happened and happening in

your career?

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Thank you very much for taking part in my research project. I will transcribe the interview

and send it to you for verification. Please note any changes you’d like to make.

` 282

Appendix 2.

Qualifications accepted as Past study at Least at Bachelor Degree Level

Inclusions.

Qualification Accreditation Body

Certified Practicing

Accountant (CPA)

CPA Australia (Degree required) Ref CPA Website

http://www.cpaaustralia.com.au/apps/careers/accreditedcourses/c

oursessearch.aspx on 19/8/2012.

Charted Accountant Institute of Chartered Accountants Australia (Degree required)

http://www.charteredaccountants.com.au/Candidates/The-

Chartered-Accountants-Program/Entry-requirements 19/8/2012

Fellow of The Royal

Australian & New Zealand

College of Psychiatrists"

The Royal Australian & new Zealand College of Psychiatrists.

(Medical Speciality)

Fellow of the Institute

Chartered Secretaries

Charted Secretaries Australia (University Qualification

Required) http://www.csaust.com/membership/become-a-

member/qualifying-and-applying-for-associate-or-fellow-

membership.aspx 19/8/2012

Fellow of Australian

Institute of Finance and

Insurance"

Australian and New Zealand Institute of Finance and Insurance

(Postgraduate Qualification Required for Fellowship)

http://www.theinstitute.com.au/Membership/Fellowship.aspx

19/8/2012

Fellow of Australian

Insurance Institute"

As Above

Fellow of Institute of

Actuaries

Actuaries Institute

http://www.actuaries.asn.au/Membership/MembershipoftheInstit

ute/Student.aspx ( Degree required)

Fellow of RACGP The Royal Australian College of General Practitioners


Fellow of Royal

Australasian College of

Physicians"

The Royal Australasian College of Physicians


Fellow of Royal Australian

College of Radiologists

The Royal Australian College of Radiologists


Fellow of Royal College

of Obstetrics and

Gynaecology

The Royal College of Obstetrics and Gynaecology


Fellow of the Australasian

Faculty of Occupational &

Environment

Australasian Faculty of Occupational and Environmental

Medicine (Medical Speciality)

Fellow of the Royal


Dental Surgeons

The Royal Australasian College of Dental Surgeons

(Bachelor of Dentistry Required)

Fellow of the Royal


Physicians (Paediatrics)

The Royal Australasian College of Physicians (Paediatrics)


Fellowship Australasian

College for Emergency

Australasian College for Emergency Medicine


http://www.cpaaustralia.com.au/apps/careers/accreditedcourses/coursessearch.aspx

http://www.cpaaustralia.com.au/apps/careers/accreditedcourses/coursessearch.aspx

http://www.charteredaccountants.com.au/Candidates/The-Chartered-Accountants-Program/Entry-requirements

http://www.charteredaccountants.com.au/Candidates/The-Chartered-Accountants-Program/Entry-requirements

http://www.csaust.com/membership/become-a-member/qualifying-and-applying-for-associate-or-fellow-membership.aspx



http://www.theinstitute.com.au/Membership/Fellowship.aspx

http://www.actuaries.asn.au/Membership/MembershipoftheInstitute/Student.aspx

http://www.actuaries.asn.au/Membership/MembershipoftheInstitute/Student.aspx

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Medicine

Fellowship BAPT (UK) British Association of Play Therapists (Honours Degree

Required)

http://www.playtherapycareers.org.uk/becomeaplaytherapist.htm

#entry 19/8/2012

http://www.bapt.info/ 19/8/2012.

Fellowship of the Royal

Australasia College of

Pathologists

The Royal Australasia College of Pathologists (Medical

Specialty)

http://www.playtherapycareers.org.uk/becomeaplaytherapist.htm#entry

http://www.playtherapycareers.org.uk/becomeaplaytherapist.htm#entry

http://www.bapt.info/

` 284

Appendix 3: Forms

1. Invitation to Participate

2. Participant Information Sheet

3. Participant Consent Form

285

School of education

CRICOS PROVIDER NUMBER 00025B

Narelle Eggins

PhD Candidate

School of education

Invitation to Participate

In my PhD research project in the School of education at The University of Queensland I am

investigating occupational change and mobility, focusing on people who have studied at university

in at least two different occupational fields.

I would like to invite anyone who has completed at least a Bachelor level degree, worked for

at least two years and then returned to university to study in a different field, to participate in my

study.

What is involved: A one hour interview at a suitable time and venue. Issues addressed in the

interview will be practical matters related to your career, issues in your decision to undertake

further tertiary study and the anticipated and/or actual outcomes of that study.

The interview will be recorded and the transcription emailed to you for checking.

Identifying information will be kept confidential and will not be published.

Please note: Because I require a balanced sample with respect to age, gender, and fields of

study, not everyone who is interested will be able to participate.

If you would like to take part in the study, I’d really like to hear from you.

[email protected]

Yours sincerely

Narelle Eggins

mailto:[email protected]

286

School of education


Participant Information Sheet

Project Title: Lifelong Career Pathway Implications of Initial Career Choices:

The Relationship between Age and Career Directional change.

Name of Researcher: Narelle Eggins

Purpose of Research

I am a PhD student in the School of education at The University of Queensland and I am

investigating the nature and extent of occupational change or attempted change-in the careers of individuals

in professional level occupations using fields of tertiary study as an indicator of field of occupation; and

practical issues associated with changing from one professional level occupation to another.

Who May Participate?

I would like to interview people who have or had professional occupations, that is occupations that

require at least a Bachelor degree qualification, and who are undertaking or have undertaken further tertiary

study in a field that is different from that of previous tertiary study.

What will I be expected to do if I participate?

Participants will be interviewed once for approximately one hour by the researcher at a suitable time

and venue. Each interview will be recorded and transcribed. A copy of the transcript will be given to the

participant to check for accuracy.

Issues addressed in the interview will be practical matters related to your career, your decision to

undertake further tertiary study and the anticipated and/or actual outcomes.

Participant’s contact details, age, gender and mode of study will be recorded but identifying

information will not be published in a research report or thesis document. Such information will be filed in a

locked filing cabinet and password protected on a computer. Codes will be assigned to each participant for

reference purposes.

“This study has been cleared in accordance with the ethical review guidelines and processes of The

University of Queensland. These guidelines are endorsed by the University's principal human ethics

committee, the Human Experimentation Ethical Review Committee, and registered with the Australian health

Ethics Committee as complying with the National Statement. You are free to discuss your participation in

this study with project staff Dr. Mary McMahon (contactable on [email protected]). If you would

like to speak to an officer of the University not involved in the study, you may contact the School Ethics

Officer on 3365 6502."

Participation is voluntary and participants may withdraw anytime without prejudice. In the event of a

participant deciding to withdraw, any data already collected from them will be destroyed if in paper form and

the computer file deleted.

Yours sincerely Narelle Eggins

287

School of education


Participant Consent Form:

I consent to participate in the research project entitled :

Lifelong Career Pathway Implications of Initial Career Choices: The Relationship between

Age and Career Directional change.

I have read the Participant Information Sheet and I understand that:

I will be interviewed once for approximately one hour by the researcher at a suitable time and venue

The interview will be recorded and transcribed.

I will be asked to check the transcript for accuracy.

Data will be filed in a locked filing cabinet and password protected on a computer.

Issues addressed in the interview will be practical matters related to my career, my decision to

undertake further tertiary study and the anticipated and/or actual outcomes.

My contact details, age, gender and mode of study will be recorded but identifying information will

not be published in a research report or thesis document.

Participation in the project is voluntary and I may withdraw at any time without prejudice.

If I withdraw from the project, any data already collected from me will be destroyed if in paper

form and the computer file deleted

Name: __________________________________________________

Contact Details: ____________________________________________________________

Signature: ___________________________________________________

288

`

Appendix 4. Ethical Clearance

SCHOOL OF EDUCATION

Response to Application for Ethical Clearance

Applicant Name: Narelle Eggins

Principal Supervisor: Mary McMahon

Applicant email address: [email protected]

Participants/Recruitment (Qs 1-3) –

Sufficient information provided.

Project Summary/Research Plan (Qs 4-5)

Very good.

Ethical Considerations (Qs 6-17)

Clearly articulated ethical considerations.

Consent Form/Information Sheet

Sufficient and appropriate content provided.

Questionnaire

Good.

Gatekeepers

Approval has been granted. Letter is contained in application.

Presentation (correct form, typed, error free)

Good.

Comments & Recommendations

The application is approved. Best of luck with your study! (Signed) Member of UQSE Research Ethics Committee:

Kim Nichols, member of UQSE Research Ethics Committee

Date. 24 January, 2012


289

`

SCHOOL OF EDUCATION

Response to Amendments to Approved Application for Ethical Clearance

Applicant Name: Narelle Eggins

Principal Supervisor: Mary McMahon

Applicant email address: [email protected]

Amendments to Participants/Recruitment (Qs 1-2) -

Sufficient information about amended participant recruitment provided.

Amendments to Project Summary/Research Plan (Q 3)

Sufficiently described.

Amendments to Ethical Considerations (Qs 4-12)

New ethical considerations have been adequately addressed.

Amendments to Consent Form/Information Sheet

New Information sheets and Consent Forms address the audience appropriately and contain

sufficient information.

Questionnaire NA

Gatekeepers NA

Presentation (correct form, typed, error free)

Presentation is good.

Comments & Recommendation

Ethical clearance is granted. Best of luck!

(Signed) Member of UQSE Research Ethics Committee:

Kim Nichols

Date. 21/12/12


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